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https://paperswithcode.com/paper/diffpr-diffusion-based-phase-reconstruction
|
2506.11183
| null | null |
DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning
|
Oversmoothing remains a persistent problem when applying deep learning to off-axis quantitative phase imaging (QPI). End-to-end U-Nets favour low-frequency content and under-represent fine, diagnostic detail. We trace this issue to spectral bias and show that the bias is reinforced by high-level skip connections that feed high-frequency features directly into the decoder. Removing those deepest skips thus supervising the network only at a low resolution significantly improves generalisation and fidelity. Building on this insight, we introduce DiffPR, a two-stage frequency-decoupled framework. Stage 1: an asymmetric U-Net with cancelled high-frequency skips predicts a quarter-scale phase map from the interferogram, capturing reliable low-frequency structure while avoiding spectral bias. Stage 2: the upsampled prediction, lightly perturbed with Gaussian noise, is refined by an unconditional diffusion model that iteratively recovers the missing high-frequency residuals through reverse denoising. Experiments on four QPI datasets (B-Cell, WBC, HeLa, 3T3) show that DiffPR outperforms strong U-Net baselines, boosting PSNR by up to 1.1 dB and reducing MAE by 11 percent, while delivering markedly sharper membrane ridges and speckle patterns. The results demonstrate that cancelling high-level skips and delegating detail synthesis to a diffusion prior is an effective remedy for the spectral bias that limits conventional phase-retrieval networks.
| null |
https://arxiv.org/abs/2506.11183v1
|
https://arxiv.org/pdf/2506.11183v1.pdf
| null |
[
"Yi Zhang"
] |
[
"Denoising",
"Diagnostic"
] | 2025-06-12T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Masked autoencoder",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Self-Supervised Learning** refers to a category of methods where we learn representations in a self-supervised way (i.e without labels). These methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. Below you can find a continuously updating list of self-supervised methods.",
"name": "Self-Supervised Learning",
"parent": null
},
"name": "MAE",
"source_title": "Masked Autoencoders Are Scalable Vision Learners",
"source_url": "https://arxiv.org/abs/2111.06377v2"
}
] |
https://paperswithcode.com/paper/grids-often-outperform-implicit-neural
|
2506.11139
| null | null |
Grids Often Outperform Implicit Neural Representations
|
Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We investigate the performance of diverse INRs across a suite of 2D and 3D real and synthetic signals with varying effective bandwidth, as well as both overfitting and generalization tasks including tomography, super-resolution, and denoising. By stratifying performance according to model size as well as signal type and bandwidth, our results shed light on how different INR and grid representations allocate their capacity. We find that, for most tasks and signals, a simple regularized grid with interpolation trains faster and to higher quality than any INR with the same number of parameters. We also find limited settings where INRs outperform grids -- namely fitting signals with underlying lower-dimensional structure such as shape contours -- to guide future use of INRs towards the most advantageous applications. Code and synthetic signals used in our analysis are available at https://github.com/voilalab/INR-benchmark.
|
Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood.
|
https://arxiv.org/abs/2506.11139v1
|
https://arxiv.org/pdf/2506.11139v1.pdf
| null |
[
"Namhoon Kim",
"Sara Fridovich-Keil"
] |
[
"Denoising",
"Super-Resolution"
] | 2025-06-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/jafar-jack-up-any-feature-at-any-resolution-1
|
2506.11136
| null | null |
JAFAR: Jack up Any Feature at Any Resolution
|
Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to produce the high-resolution modalities required for downstream tasks. In this work, we introduce JAFAR, a lightweight and flexible feature upsampler that enhances the spatial resolution of visual features from any Foundation Vision Encoder to an arbitrary target resolution. JAFAR employs an attention-based module designed to promote semantic alignment between high-resolution queries, derived from low-level image features, and semantically enriched low-resolution keys, using Spatial Feature Transform (SFT) modulation. Notably, despite the absence of high-resolution supervision, we demonstrate that learning at low upsampling ratios and resolutions generalizes remarkably well to significantly higher output scales. Extensive experiments show that JAFAR effectively recovers fine-grained spatial details and consistently outperforms existing feature upsampling methods across a diverse set of downstream tasks. Project page at https://jafar-upsampler.github.io
|
Foundation Vision Encoders have become essential for a wide range of dense vision tasks.
|
https://arxiv.org/abs/2506.11136v1
|
https://arxiv.org/pdf/2506.11136v1.pdf
| null |
[
"Paul Couairon",
"Loick Chambon",
"Louis Serrano",
"Jean-Emmanuel Haugeard",
"Matthieu Cord",
"Nicolas Thome"
] |
[
"Feature Upsampling"
] | 2025-06-10T00:00:00 |
https://arxiv.org/abs/2506.11136
|
https://arxiv.org/pdf/2506.11136
|
jafar-jack-up-any-feature-at-any-resolution
| null |
[
{
"code_snippet_url": "20",
"description": "**Spatial Feature Transform**, or **SFT**, is a layer that generates affine transformation parameters for spatial-wise feature modulation, and was originally proposed within the context of image super-resolution. A Spatial Feature Transform (SFT) layer learns a mapping function $\\mathcal{M}$ that outputs a modulation parameter pair $(\\mathbf{\\gamma}, \\mathbf{\\beta})$ based on some prior condition $\\Psi$. The learned parameter pair adaptively influences the outputs by applying an affine transformation spatially to each intermediate feature maps in an SR network. During testing, only a single forward pass is needed to generate the HR image given the LR input and segmentation probability maps.\r\n\r\nMore precisely, the prior $\\Psi$ is modeled by a pair of affine transformation parameters $(\\mathbf{\\gamma}, \\mathbf{\\beta})$ through a mapping function $\\mathcal{M}: \\Psi \\mapsto(\\mathbf{\\gamma}, \\mathbf{\\beta})$. Consequently,\r\n\r\n$$\r\n\\hat{\\mathbf{y}}=G_{\\mathbf{\\theta}}(\\mathbf{x} \\mid \\mathbf{\\gamma}, \\mathbf{\\beta}), \\quad(\\mathbf{\\gamma}, \\mathbf{\\beta})=\\mathcal{M}(\\Psi)\r\n$$\r\n\r\nAfter obtaining $(\\mathbf{\\gamma}, \\mathbf{\\beta})$ from conditions, the transformation is carried out by scaling and shifting feature maps of a specific layer:\r\n\r\n$$\r\n\\operatorname{SFT}(\\mathbf{F} \\mid \\mathbf{\\gamma}, \\mathbf{\\beta})=\\mathbf{\\gamma} \\odot \\mathbf{F}+\\mathbf{\\beta}\r\n$$\r\n\r\nwhere $\\mathbf{F}$ denotes the feature maps, whose dimension is the same as $\\gamma$ and $\\mathbf{\\beta}$, and $\\odot$ is referred to element-wise multiplication, i.e., Hadamard product. Since the spatial dimensions are preserved, the SFT layer not only performs feature-wise manipulation but also spatial-wise transformation.",
"full_name": "Spatial Feature Transform",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Spatial Feature Transform",
"source_title": "Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform",
"source_url": "http://arxiv.org/abs/1804.02815v1"
},
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/contextloss-context-information-for-topology
|
2506.11134
| null | null |
ContextLoss: Context Information for Topology-Preserving Segmentation
|
In image segmentation, preserving the topology of segmented structures like vessels, membranes, or roads is crucial. For instance, topological errors on road networks can significantly impact navigation. Recently proposed solutions are loss functions based on critical pixel masks that consider the whole skeleton of the segmented structures in the critical pixel mask. We propose the novel loss function ContextLoss (CLoss) that improves topological correctness by considering topological errors with their whole context in the critical pixel mask. The additional context improves the network focus on the topological errors. Further, we propose two intuitive metrics to verify improved connectivity due to a closing of missed connections. We benchmark our proposed CLoss on three public datasets (2D & 3D) and our own 3D nano-imaging dataset of bone cement lines. Training with our proposed CLoss increases performance on topology-aware metrics and repairs up to 44% more missed connections than other state-of-the-art methods. We make the code publicly available.
| null |
https://arxiv.org/abs/2506.11134v1
|
https://arxiv.org/pdf/2506.11134v1.pdf
| null |
[
"Benedict Schacht",
"Imke Greving",
"Simone Frintrop",
"Berit Zeller-Plumhoff",
"Christian Wilms"
] |
[
"Image Segmentation",
"Semantic Segmentation"
] | 2025-06-10T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/integrating-movable-antennas-and-intelligent
|
2506.21375
| null | null |
Integrating Movable Antennas and Intelligent Reflecting Surfaces for Coverage Enhancement
|
This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple designated target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS reflection coefficients, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, we propose three coverage-enhancement schemes: the area-adaptive MA-IRS scheme, the area-adaptive MA-staIRS scheme, and the shared MA-staIRS scheme, where staIRS denotes static IRSs with reflection coefficients configured only once during installation. These schemes lead to challenging non-convex optimization problems with implicit objective functions, which are difficult to solve optimally. To address these problems, we propose a general algorithmic framework that can be applied to solve each problem efficiently albeit suboptimally. Simulation results demonstrate that: 1) the proposed MA-based schemes consistently outperform their fixed-position antenna (FPA)-based counterparts under both area-adaptive and static IRS configurations, with the area-adaptive MA-IRS scheme achieving the best worst-case SNR performance; 2) as transmit antennas are typically far fewer than IRS elements, the area-adaptive MA-staIRS scheme may underperform the baseline FPA scheme with area-adaptive IRSs in terms of the worst-case SNR, but a modest increase in antenna number can reverse this trend; 3) under a fixed total cost, the optimal MA-to-IRS-element ratio for the worst-case SNR maximization is empirically found to be proportional to the reciprocal of their unit cost ratio.
| null |
https://arxiv.org/abs/2506.21375v1
|
https://arxiv.org/pdf/2506.21375v1.pdf
| null |
[
"Ying Gao",
"Qingqing Wu",
"Weidong Mei",
"Guangji Chen",
"Wen Chen",
"Ziyuan Zheng"
] |
[] | 2025-06-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/localization-based-beam-focusing-in-near
|
2506.21325
| null | null |
Localization-Based Beam Focusing in Near-Field Communications
|
Shifting 6G-and-beyond wireless communication systems to higher frequency bands and the utilization of massive multiple-input multiple-output arrays will extend the near-field region, affecting beamforming and user localization schemes. In this paper, we propose a localization-based beam-focusing strategy that leverages the dominant line-of-sight (LoS) propagation arising at mmWave and sub-THz frequencies. To support this approach, we analyze the 2D-MUSIC algorithm for distance estimation by examining its spectrum in simplified, tractable setups with minimal numbers of antennas and users. Lastly, we compare the proposed localization-based beam focusing, with locations estimated via 2D-MUSIC, with zero forcing with pilot-based channel estimation in terms of uplink sum spectral efficiency. Our numerical results show that the proposed method becomes more effective under LoS-dominated propagation, short coherence blocks, and strong noise power arising at high carrier frequencies and with large bandwidths.
| null |
https://arxiv.org/abs/2506.21325v1
|
https://arxiv.org/pdf/2506.21325v1.pdf
| null |
[
"Nima Mozaffarikhosravi",
"Prathapasinghe Dharmawansa",
"Italo Atzeni"
] |
[] | 2025-06-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adversarial-training-enhancing-out-of
|
2506.21208
| null | null |
Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation
|
Deep neural networks (DNNs) have widespread applications for optimizing resource allocation. Yet, their performance is vulnerable to distribution shifts between training and test data, say channels. In this letter, we resort to adversarial training (AT) for enhancing out-of-distribution (OOD) generalizability of DNNs trained in unsupervised manner. We reformulate AT to capture the OOD degradation, and propose a one-step gradient ascent method for AT. The proposed method is validated by optimizing hybrid precoding. Simulation results showcase the enhanced OOD performance of multiple kinds of DNNs across various channel distributions, when only Rayleigh fading channels are used for training.
| null |
https://arxiv.org/abs/2506.21208v1
|
https://arxiv.org/pdf/2506.21208v1.pdf
| null |
[
"ShengJie Liu",
"Chenyang Yang"
] |
[
"Out-of-Distribution Generalization"
] | 2025-06-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/medprompt-llm-cnn-fusion-with-weight-routing
|
2506.21199
| null | null |
MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification
|
Current medical image analysis systems are typically task-specific, requiring separate models for classification and segmentation, and lack the flexibility to support user-defined workflows. To address these challenges, we introduce MedPrompt, a unified framework that combines a few-shot prompted Large Language Model (Llama-4-17B) for high-level task planning with a modular Convolutional Neural Network (DeepFusionLab) for low-level image processing. The LLM interprets user instructions and generates structured output to dynamically route task-specific pretrained weights. This weight routing approach avoids retraining the entire framework when adding new tasks-only task-specific weights are required, enhancing scalability and deployment. We evaluated MedPrompt across 19 public datasets, covering 12 tasks spanning 5 imaging modalities. The system achieves a 97% end-to-end correctness in interpreting and executing prompt-driven instructions, with an average inference latency of 2.5 seconds, making it suitable for near real-time applications. DeepFusionLab achieves competitive segmentation accuracy (e.g., Dice 0.9856 on lungs) and strong classification performance (F1 0.9744 on tuberculosis). Overall, MedPrompt enables scalable, prompt-driven medical imaging by combining the interpretability of LLMs with the efficiency of modular CNNs.
| null |
https://arxiv.org/abs/2506.21199v1
|
https://arxiv.org/pdf/2506.21199v1.pdf
| null |
[
"Shadman Sobhan",
"Kazi Abrar Mahmud",
"Abduz Zami"
] |
[
"Image Segmentation",
"Large Language Model",
"Medical Image Analysis",
"Medical Image Segmentation",
"Semantic Segmentation",
"Task Planning"
] | 2025-06-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/arsar-net-intelligent-sar-imaging-with
|
2506.18324
| null | null |
ARSAR-Net: Intelligent SAR Imaging with Adaptive Regularization
|
Deep unfolding networks have recently emerged as a promising approach for synthetic aperture radar (SAR) imaging. However, baseline unfolding networks, typically derived from iterative reconstruction algorithms such as the alternating direction method of multipliers (ADMM), lack generalization capability across scenes, primarily because their regularizers are empirically designed rather than learned from data. In this study, we introduce a learnable regularizer into the unfolding network and propose a SAR imaging network with adaptive regularization (ARSAR-Net), which aims to generalize across heterogeneous scenes including offshore ships, islands, urban areas, and mountainous terrain. Furthermore, two variants of ARSAR-Net are developed, targeting improved imaging efficiency and reconstruction quality, respectively. Extensive validation through simulated and real-data experiments demonstrates three key advantages of ARSAR-Net: (1) a 50% increase in imaging speed over existing unfolding networks, (2) a PSNR gain of up to 2.0 dB in imaging quality, and (3) enhanced adaptability to complex scenes. These advancements establish a new paradigm for computationally efficient and generalizable SAR imaging systems.
| null |
https://arxiv.org/abs/2506.18324v2
|
https://arxiv.org/pdf/2506.18324v2.pdf
| null |
[
"Shiping Fu",
"Yufan Chen",
"Zhe Zhang",
"Xiaolan Qiu",
"Qixiang Ye"
] |
[] | 2025-06-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/characterization-of-rydberg-atom-signal
|
2506.21123
| null | null |
Characterization of Rydberg-Atom Signal Reception of Dual-Frequency Signals Coupled with Two Energy Levels
|
Rydberg atomic sensors have been adopted for novel radio frequency (RF) measurement technique and the sensing capability for signals in multiple frequencies makes it attractive for multi-user communication. However, unlike traditional antennas where the signals in multiple frequencies are orthogonal, the received signals of atomic sensors corresponding to different energy levels will be downconverted to the baseband simultaneously, resulting in multi-user interference. Thus, in this paper, we analyze the mutual interference characteristics of two RF signals with different carrier frequencies coupling different energy levels. We introduce the joint response coefficient based on the receiver characteristics and analyze the interference of one user to another. We analyze the bit-error rate (BER) and symbol-error rate (SER) for two signals coupling two different energy levels. We also conduct experiments to validate the BER and SER results.
| null |
https://arxiv.org/abs/2506.21123v1
|
https://arxiv.org/pdf/2506.21123v1.pdf
| null |
[
"Hao Wu",
"Chongwu Xie",
"Xinyuan Yao",
"Kang-Da Wu",
"Shanchi Wu",
"Rui Ni",
"Guo-Yong Xiang",
"Chen Gong"
] |
[] | 2025-06-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/point-cloud-environment-based-channel
|
2506.21112
| null | null |
Point Cloud Environment-Based Channel Knowledge Map Construction
|
Channel knowledge map (CKM) provides certain levels of channel state information (CSI) for an area of interest, serving as a critical enabler for environment-aware communications by reducing the overhead of frequent CSI acquisition. However, existing CKM construction schemes adopt over-simplified environment information, which significantly compromises their accuracy. To address this issue, this work proposes a joint model- and data-driven approach to construct CKM by leveraging point cloud environmental data along with a few samples of location-tagged channel information. First, we propose a novel point selector to identify subsets of point cloud that contain environmental information relevant to multipath channel gains, by constructing a set of co-focal ellipsoids based on different time of arrival (ToAs). Then, we trained a neural channel gain estimator to learn the mapping between each selected subset and its corresponding channel gain, using a real-world dataset we collected through field measurements, comprising environmental point clouds and corresponding channel data. Finally, experimental results demonstrate that: For CKM construction of power delay profile (PDP), the proposed method achieves a root mean squared error (RMSE) of 2.95 dB, significantly lower than the 7.32 dB achieved by the conventional ray-tracing method; for CKM construction of received power values, i.e., radio map, it achieves an RMSE of 1.04 dB, surpassing the Kriging interpolation method with an RMSE of 1.68 dB.
| null |
https://arxiv.org/abs/2506.21112v1
|
https://arxiv.org/pdf/2506.21112v1.pdf
| null |
[
"Yancheng Wang",
"Wei Guo",
"GuanYing Chen",
"Ye Zhang",
"Shuguang Cui"
] |
[] | 2025-06-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
},
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "ADaptive gradient method with the OPTimal convergence rate",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "ADOPT",
"source_title": "ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate",
"source_url": "https://arxiv.org/abs/2411.02853v3"
}
] |
https://paperswithcode.com/paper/analysis-of-null-related-beampattern-measures
|
2506.21043
| null | null |
Analysis of Null Related Beampattern Measures and Signal Quantization Effects for Linear Differential Microphone Arrays
|
A differential microphone array (DMA) offers enhanced capabilities to obtain sharp nulls at the cost of relatively broad peaks in the beam power pattern. This can be used for applications that require nullification or attenuation of interfering sources. To the best of our knowledge, the existing literature lacks measures that directly assess the efficacy of nulls, and null-related measures have not been investigated in the context of differential microphone arrays (DMAs). This paper offers new insights about the utility of DMAs by proposing measures that characterize the nulls in their beam power patterns. We investigate the performance of differential beamformers by presenting and evaluating null-related measures namely null depth (ND) and Null Width (NW) as a function of depth level relative to the beam power pattern maxima. A study of signal quantization effects due to data acquisition for 1st, 2nd and 3rd order linear DMAs and for different beampatterns i.e. dipole, cardioid, hypercardioid and supercardioid is presented. An analytical expression for the quantized beamformed output for any general $ N^{th} $ order DMA is formulated. Simulation results of the variation of ND with number of quantization bits and the variation of NW as a function of depth are also presented and inferences are drawn. Lab experiments are conducted in a fully anechoic room to support the simulation results. The measured beampattern exhibits a pronounced null depth, confirming the effectiveness of the experimental setup.
| null |
https://arxiv.org/abs/2506.21043v1
|
https://arxiv.org/pdf/2506.21043v1.pdf
| null |
[
"Shweta Pal",
"Arun Kumar",
"Monika Agrawal"
] |
[
"Quantization"
] | 2025-06-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In image inpainting task, the mechanism extracts complementary features from the word embedding in two paths by reciprocal attention, which is done by comparing the descriptive text and complementary image areas through reciprocal attention.",
"full_name": "Dual Multimodal Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "DMA",
"source_title": "Text-Guided Neural Image Inpainting",
"source_url": "https://arxiv.org/abs/2004.03212v4"
}
] |
https://paperswithcode.com/paper/co-design-of-sensing-communications-and
|
2506.20970
| null | null |
Co-Design of Sensing, Communications, and Control for Low-Altitude Wireless Networks
|
The rapid advancement of Internet of Things (IoT) services and the evolution toward the sixth generation (6G) have positioned unmanned aerial vehicles (UAVs) as critical enablers of low-altitude wireless networks (LAWNs). This work investigates the co-design of integrated sensing, communication, and control ($\mathbf{SC^{2}}$) for multi-UAV cooperative systems with finite blocklength (FBL) transmission. In particular, the UAVs continuously monitor the state of the field robots and transmit their observations to the robot controller to ensure stable control while cooperating to localize an unknown sensing target (ST). To this end, a weighted optimization problem is first formulated by jointly considering the control and localization performance in terms of the linear quadratic regulator (LQR) cost and the determinant of the Fisher information matrix (FIM), respectively. The resultant problem, optimizing resource allocations, the UAVs' deployment positions, and multi-user scheduling, is non-convex. To circumvent this challenge, we first derive a closed-form expression of the LQR cost with respect to other variables. Subsequently, the non-convex optimization problem is decomposed into a series of sub-problems by leveraging the alternating optimization (AO) approach, in which the difference of convex functions (DC) programming and projected gradient descent (PGD) method are employed to obtain an efficient near-optimal solution. Furthermore, the convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Extensive simulation results are presented to validate the effectiveness of our proposed approach compared to the benchmark schemes and reveal the trade-off between control and sensing performance.
| null |
https://arxiv.org/abs/2506.20970v1
|
https://arxiv.org/pdf/2506.20970v1.pdf
| null |
[
"Haijia Jin",
"Jun Wu",
"Weijie Yuan",
"Fan Liu",
"Yuanhao Cui"
] |
[
"Scheduling"
] | 2025-06-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/doppler-estimation-and-compensation
|
2506.20858
| null | null |
Doppler Estimation and Compensation Techniques in LoRa Direct-to-Satellite Communications
|
Within the LPWAN framework, the LoRa modulation adopted by LoRaWAN technology has garnered significant interest as a connectivity solution for IoT applications due to its ability to offer low-cost, low-power, and long-range communications. One emerging use case of LoRa is DtS connectivity, which extends coverage to remote areas for supporting IoT operations. The satellite IoT industry mainly prefers LEO because it has lower launch costs and less path loss compared to Geostationary orbit. However, a major drawback of LEO satellites is the impact of the Doppler effect caused by their mobility. Earlier studies have confirmed that the Doppler effect significantly degrades the LoRa DtS performance. In this paper, we propose four frameworks for Doppler estimation and compensation in LoRa DtS connectivity and numerically compare the performance against the ideal scenario without the Doppler effect. Furthermore, we investigate the trade-offs among these frameworks by analyzing the interplay between spreading factor, and other key parameters related to the Doppler effect. The results provide insights into how to achieve robust LoRa configurations for DtS connectivity.
| null |
https://arxiv.org/abs/2506.20858v1
|
https://arxiv.org/pdf/2506.20858v1.pdf
| null |
[
"Jamil Farhat",
"Gianni Pasolini",
"Enrico Paolini",
"Muhammad Asad Ullah",
"Richard Demo Souza"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/coherent-track-before-detect
|
2506.18177
| null | null |
Coherent Track-Before-Detect
|
Accurately tracking an unknown and time-varying number of objects in complex environments is a significant challenge but a fundamental capability in a variety of applications, including applied ocean sciences, surveillance, autonomous driving, and wireless communications. Conventional Bayesian multiobject tracking (MOT) methods typically employ a detect-then-track (DTT) approach, where a frontend detector preprocesses raw sensor data to extract measurements for MOT. The irreversible nature of this preprocessing step can discard valuable object-related information, particularly impairing the ability to resolve weak or closely spaced objects. The track-before-detect (TBD) paradigm offers an alternative by operating directly on sensor data. However, existing TBD approaches introduce simplifications to facilitate the development of inference methods, such as assuming known signal amplitudes or conditional independence between sensor measurements given object states. These assumptions can lead to suboptimal performance and limit the applicability of the resulting TBD methods in realistic scenarios. This paper introduces coherent TBD based on a comprehensive signal model for sensor data. The new model accounts for sensor data correlations and amplitude fluctuations, enabling the accurate representation of the physics of the data-generating process in TBD. Coherent TBD is suitable for a wide range of problems in active and passive radar, active and passive sonar, as well as integrated sensing and communication systems. Based on a factor graph representation of the new measurement model, a scalable belief propagation (BP) method is developed to perform efficient Bayesian inference. Experimental results, performed with both synthetic and real data, demonstrate that the proposed method outperforms state-of-the-art conventional MOT methods.
| null |
https://arxiv.org/abs/2506.18177v2
|
https://arxiv.org/pdf/2506.18177v2.pdf
| null |
[
"Mingchao Liang",
"Florian Meyer"
] |
[
"Autonomous Driving",
"Bayesian Inference",
"Integrated sensing and communication"
] | 2025-06-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multi-objective-reinforcement-learning-for-6
|
2506.20853
| null | null |
Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
|
The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find Pareto-optimal solutions and compare deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms. Our results demonstrate the effectiveness of both algorithms in adapting to various scenarios, with SAC showing improved stability and sample efficiency compared to DDPG. We further employ the NSGA-II algorithm to estimate an upper bound on the Pareto front of the considered problem. This work contributes to the development of more efficient and adaptive cognitive radar systems capable of balancing multiple competing objectives in dynamic environments.
| null |
https://arxiv.org/abs/2506.20853v1
|
https://arxiv.org/pdf/2506.20853v1.pdf
| null |
[
"Ziyang Lu",
"Subodh Kalia",
"M. Cenk Gursoy",
"Chilukuri K. Mohan",
"Pramod K. Varshney"
] |
[
"Deep Reinforcement Learning",
"Management",
"Multi-Objective Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning"
] | 2025-06-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\\_{t} = \\left(s\\_{t}, a\\_{t}, r\\_{t}, s\\_{t+1}\\right)$ in a data-set $D = e\\_{1}, \\cdots, e\\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.\r\n\r\nImage Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)",
"full_name": "Experience Replay",
"introduced_year": 1993,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Experience Replay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**DDPG**, or **Deep Deterministic Policy Gradient**, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from [DQNs](https://paperswithcode.com/method/dqn): in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with [batch normalization](https://paperswithcode.com/method/batch-normalization).",
"full_name": "Deep Deterministic Policy Gradient",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "DDPG",
"source_title": "Continuous control with deep reinforcement learning",
"source_url": "https://arxiv.org/abs/1509.02971v6"
}
] |
https://paperswithcode.com/paper/compact-analytical-model-for-real-time
|
2506.20823
| null | null |
Compact Analytical Model for Real-Time Evaluation of OAM-Based Inter-Satellite Links
|
This paper presents an efficient analytical framework for evaluating the performance of inter-satellite communication systems utilizing orbital angular momentum (OAM) beams under pointing errors. An accurate analytical model is first developed to characterize intermodal crosstalk caused by beam misalignment in OAM-based inter-satellite links. Building upon this model, we derive efficient expressions to analyze and optimize system performance in terms of bit error rate (BER). Unlike traditional Monte Carlo-based methods that are computationally intensive, the proposed approach offers accurate performance predictions. This enables a substantial decrease in computation time while maintaining high accuracy, thanks to the use of analytical expressions for both crosstalk and BER. This fast and accurate evaluation capability is particularly critical for dynamic low Earth orbit (LEO) satellite constellations, where network topology and channel conditions change rapidly, requiring real-time link adaptation. Furthermore, we systematically design and evaluate asymmetric OAM mode sets, which significantly outperform symmetric configurations in the presence of pointing errors. Our results also reveal key insights into the interaction between beam divergence, tracking accuracy, and link distance, demonstrating that the proposed framework enables real-time optimization of system parameters with high fidelity. The analytical findings are rigorously validated against extensive Monte Carlo simulations, confirming their practical applicability for high-mobility optical wireless systems such as LEO satellite networks.
| null |
https://arxiv.org/abs/2506.20823v1
|
https://arxiv.org/pdf/2506.20823v1.pdf
| null |
[
"Mohammad Taghi Dabiri",
"Mazen Hasna"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/physical-limits-of-entanglement-based-quantum
|
2506.20798
| null | null |
Physical Limits of Entanglement-Based Quantum Key Distribution over Long-Distance Satellite Links
|
Entanglement-based quantum key distribution (QKD) protocols, such as E91 and BBM92, offer strong information-theoretic security and are naturally suited for satellite-to-satellite QKD (SatQKD) links. However, implementing these protocols over long-distance inter-satellite free-space optical (FSO) channels poses critical physical-layer challenges that are not addressed in the existing literature. In particular, photon losses due to beam divergence, pointing errors, and background noise can severely degrade the key generation rate and quantum bit error rate (QBER), especially under narrow receiver field-of-view (FoV) constraints. This paper presents a comprehensive performance analysis of entanglement-based inter-satellite QKD, focusing on photon-level modeling and the impact of practical impairments. We develop analytical expressions for signal detection probabilities, background photon influence, multi-pair emissions, and QBER, incorporating key parameters such as link distance, transmitter tracking jitter, receiver misalignment, and photon pair generation rate. Simulation results reveal the nonlinear sensitivity of system performance to tracking error and FoV limitations, and highlight optimal parameter regimes that jointly maximize secret key rate while maintaining QBER below acceptable thresholds. The proposed model provides actionable design insights for reliable and efficient deployment of entanglement-based SatQKD systems.
| null |
https://arxiv.org/abs/2506.20798v1
|
https://arxiv.org/pdf/2506.20798v1.pdf
| null |
[
"Mohammad Taghi Dabiri",
"Mazen Hasna",
"Saif Al-Kuwari",
"Khalid Qaraqe"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/precise-near-field-beam-training-with-dft
|
2506.20783
| null | null |
Precise Near-Field Beam Training with DFT Codebook based on Amplitude-only Measurement
|
Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training and signal processing design. In this work, we present a low-complexity near-field beam training scheme that fully utilizes the conventional discrete Fourier transform (DFT) codebook designed for far-field users. We begin by analyzing the received beam pattern in the near field and derive closed-form expressions for the beam width and central gain. These analytical results enable the definition of an angle-dependent, modified Rayleigh distance, which effectively distinguishes near-field and far-field user regimes. Building on the analysis, we develop a direct and computationally efficient method to estimate user distance, with a complexity of O(1), and further improve its accuracy through a simple refinement. Simulation results demonstrate significant gains in both single- and multi-user settings, with up to 2.38 dB SNR improvement over exhaustive search. To further enhance estimation accuracy, we additionally propose a maximum likelihood estimation (MLE) based refinement method, leveraging the Rician distribution of signal amplitudes and achieving accuracy close to the Cramer--Rao bound (CRB). Simulation shows the single-user and multi-user achievable rates can both approach those obtained with ideal channel state information.
| null |
https://arxiv.org/abs/2506.20783v1
|
https://arxiv.org/pdf/2506.20783v1.pdf
| null |
[
"Zijun Wang",
"Shawn Tsai",
"Rama Kiran",
"Rui Zhang"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mc-for-agriculture-a-framework-for-nature
|
2506.20637
| null | null |
MC for Agriculture: A Framework for Nature-inspired Sustainable Pest Control
|
In agriculture, molecular communication (MC) is envisioned as a framework to address critical challenges such as smart pest control. While conventional approaches mostly rely on synthetic plant protection products, posing high risks for the environment, harnessing plant signaling processes can lead to innovative approaches for nature-inspired sustainable pest control. In this paper, we investigate an approach for sustainable pest control and reveal how the MC paradigm can be employed for analysis and optimization. In particular, we consider a system where herbivore-induced plant volatiles (HIPVs), specifically methyl salicylate (MeSA), is encapsulated into microspheres deployed on deployed on plant leaves. The controlled release of MeSA from the microspheres, acting as transmitters (TXs), supports pest deterrence and antagonist attraction, providing an eco-friendly alternative to synthetic plant protection products. Based on experimental data, we investigate the MeSA release kinetics and obtain an analytical model. To describe the propagation of MeSA in farming environments, we employ a three dimensional (3D) advection-diffusion model, incorporating realistic wind fields which are predominantly affecting particle propagation, and solve it by a finite difference method (FDM). The proposed model is used to investigate the MeSA distribution for different TX arrangements, representing different practical microsphere deployment strategies. Moreover, we introduce the coverage effectiveness index (CEI) as a novel metric to quantify the environmental coverage of MeSA. This analysis offers valuable guidance for the practical development of microspheres and their deployment aimed at enhancing coverage and, consequently, the attraction of antagonistic insects.
| null |
https://arxiv.org/abs/2506.20637v1
|
https://arxiv.org/pdf/2506.20637v1.pdf
| null |
[
"Fardad Vakilipoor",
"Nora Hirschmann",
"Julian Schladt",
"Stefan Schwab",
"Annette Reineke",
"Robert Schober",
"Kathrin Castiglione",
"Maximilian Schaefer"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/differential-transformer-driven-6g-physical
|
2506.20597
| null | null |
Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
|
The emergence of 6G wireless networks promises to revolutionize vehicular communications by enabling ultra-reliable, low-latency, and high-capacity data exchange. In this context, collaborative perception techniques, where multiple vehicles or infrastructure nodes cooperate to jointly receive and decode transmitted signals, aim to enhance reliability and spectral efficiency for Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an end-to-end wireless neural receiver based on a Differential Transformer architecture, tailored for 6G V2X communication with a specific focus on enabling collaborative perception among connected autonomous vehicles. Our model integrates key components of the 6G physical layer, designed to boost performance in dynamic and challenging autonomous driving environments. We validate the proposed system across a range of scenarios, including 3GPP-defined Urban Macro (UMa) channel. To assess the model's real-world applicability, we evaluate its robustness within a V2X framework. In a collaborative perception scenario, our system processes heterogeneous LiDAR and camera data from four connected vehicles in dynamic cooperative vehicular networks. The results show significant improvements over state-of-the-art methods, achieving an average precision of 0.84, highlighting the potential of our proposed approach to enable robust, intelligent, and adaptive wireless cooperation for next-generation connected autonomous vehicles.
| null |
https://arxiv.org/abs/2506.20597v1
|
https://arxiv.org/pdf/2506.20597v1.pdf
| null |
[
"Soheyb Ribouh",
"Osama Saleem",
"Mohamed Ababsa"
] |
[
"Autonomous Driving",
"Autonomous Vehicles"
] | 2025-06-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/revisiting-champagne-sparse-bayesian-learning
|
2506.20534
| null | null |
Revisiting CHAMPAGNE: Sparse Bayesian Learning as Reweighted Sparse Coding
|
This paper revisits the CHAMPAGNE algorithm within the Sparse Bayesian Learning (SBL) framework and establishes its connection to reweighted sparse coding. We demonstrate that the SBL objective can be reformulated as a reweighted $\ell_{21}$-minimization problem, providing a more straightforward interpretation of the sparsity mechanism and enabling the design of an efficient iterative algorithm. Additionally, we analyze the behavior of this reformulation in the low signal-to-noise ratio (SNR) regime, showing that it simplifies to a weighted $\ell_{21}$-regularized least squares problem. Numerical experiments validate the proposed approach, highlighting its improved computational efficiency and ability to produce exact sparse solutions, particularly in simulated MEG source localization tasks.
| null |
https://arxiv.org/abs/2506.20534v1
|
https://arxiv.org/pdf/2506.20534v1.pdf
| null |
[
"Dylan Sechet",
"Matthieu Kowalski",
"Samy Mokhtari",
"Bruno Torrésani"
] |
[
"Computational Efficiency"
] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/active-ris-enabled-nlos-leo-satellite
|
2506.20424
| null | null |
Active RIS Enabled NLoS LEO Satellite Communications: A Three-timescale Optimization Framework
|
In this letter, we study an active reconfigurable intelligent surfaces (RIS) assisted Low Earth orbit (LEO) satellite communications under non-line-of-sight (NLoS) scenarios, where the active RIS is deployed to create visual line-of-sight links for reliable communication. To address the challenges of high energy consumption caused by frequent beamforming updates in active RIS, we propose a three-timescale optimization framework that jointly designs the transmit beamforming, RIS beamforming, and RIS direction vectors based on their characteristics. The goal is to maximize the system achievable rate while reducing energy consumption by controlling the RIS beamforming switching frequency. Then, a two-layer solution framework is developed, incorporating fractional programming (FP), alternating optimization (AO), successive approximation (SCA), and penalty-based methods, to obtain the optimized solution. Simulation results demonstrate that the proposed scheme can effectively improve system performance and reduce the energy consumption of the active RIS.
| null |
https://arxiv.org/abs/2506.20424v1
|
https://arxiv.org/pdf/2506.20424v1.pdf
| null |
[
"Ziwei Liu",
"JunYan He",
"Shanshan Zhao",
"Meng Hua",
"Bin Lyu",
"Xinjie Zhao",
"Gengxin Zhang"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/analog-ofdm-based-on-real-time-fourier
|
2506.20287
| null | null |
Analog OFDM based on Real-Time Fourier Transformation
|
This paper proposes an analog orthogonal frequency division multiplexing (OFDM) architecture based on the real-time Fourier transform (RTFT). The core enabling component is a linear-chirp phaser with engineered group velocity dispersion (GVD), which realizes RTFT and performs frequency-to-time mapping in the analog domain. In this architecture, conventional digital fast Fourier transform (FFT) and inverse FFT (IFFT) processors are replaced by two linear-chirp phasers with opposite group delay dispersions, respectively. Theoretical analysis demonstrates that, under specific phaser conditions, the OFDM signal generated by the RTFT-based analog system is mathematically equivalent to that of a conventional digital OFDM system. This equivalence is further supported by simulation results, which confirm accurate symbol transmission and recovery, as well as robustness to multipath fading when a prefix is applied. Benefiting from the use of passive microwave components, the analog OFDM system offers ultra-fast processing with reduced power consumption. Overall, this work establishes a foundation for fully analog or hybrid analog-digital OFDM system, offering a promising solution for next-generation high-speed, wideband, and energy-efficient wireless communication platforms.
| null |
https://arxiv.org/abs/2506.20287v1
|
https://arxiv.org/pdf/2506.20287v1.pdf
| null |
[
"Xiaolu Yang",
"Oscar Céspedes Vicente",
"Christophe Caloz"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/superimposed-dmrs-for-spectrally-efficient-6g
|
2506.20248
| null | null |
Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers
|
Fifth-generation (5G) systems utilize orthogonal demodulation reference signals (DMRS) to enable channel estimation at the receiver. These orthogonal DMRS-also referred to as pilots-are effective in avoiding pilot contamination and interference from both the user's own data and that of others. However, this approach incurs a significant overhead, as a substantial portion of the time-frequency resources must be reserved for pilot transmission. Moreover, the overhead increases with the number of users and transmission layers. To address these limitations in the context of emerging sixth-generation (6G) systems and to support data transmission across the entire time-frequency grid, the superposition of data and DMRS symbols has been explored as an alternative DMRS transmission strategy. In this study, we propose an enhanced version of DeepRx, a deep convolutional neural network (CNN)-based receiver, capable of estimating the channel from received superimposed (SI) DMRS symbols and reliably detecting the transmitted data. We also design a conventional receiver for comparison, which estimates the channel from SI DMRS using classical signal processing techniques. Extensive evaluations in both uplink single-user and multi-user scenarios demonstrate that DeepRx consistently outperforms the conventional receivers in terms of performance.
| null |
https://arxiv.org/abs/2506.20248v1
|
https://arxiv.org/pdf/2506.20248v1.pdf
| null |
[
"Sajad Rezaie",
"Mikko Honkala",
"Dani Korpi",
"Dick Carrillo Melgarejo",
"Tomasz Izydorczyk",
"Dimitri Gold",
"Oana-Elena Barbu"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/time-and-covariance-smoothing-for-restoration
|
2506.20237
| null | null |
Time and covariance smoothing for restoration of bivariate signals
|
In many applications and physical phenomena, bivariate signals are polarized, i.e. they trace an elliptical trajectory over time when viewed in the 2D planes of their two components. The smooth evolution of this elliptical trajectory, called polarization ellipse, is highly informative to solve ill-posed inverse problems involving bivariate signals where the signal is collected through indirect, noisy or incomplete measurements. This work proposes a novel formulation and an efficient algorithm for reconstructing bivariate signals with polarization regularization. The proposed formulation leverages the compact representation of polarization through the instantaneous covariance matrices. To address the resulting quartic optimization problem, we propose a well-suited parameter splitting strategy which leads to an efficient iterative algorithm (alternating direction method of multipliers (ADMM)) with convex subproblems at each iteration. The performance of the proposed method is illustrated on numerical synthetic data experiments.
| null |
https://arxiv.org/abs/2506.20237v1
|
https://arxiv.org/pdf/2506.20237v1.pdf
| null |
[
"Yusuf Yigit Pilavci",
"Pierre Palud",
"Julien Flamant",
"Pierre-Antoine Thouvenin",
"Jérémie Boulanger",
"Pierre Chainais"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sensing-aware-transmit-waveform-receive
|
2506.20231
| null | null |
Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems
|
In this letter, we study the problem of cooperative sensing design for an orthogonal frequency division multiplexing (OFDM) multiple base stations (MBS) system. We consider a practical scenario where the base stations (BSs) exploit certain subcarriers to realize a sensing function. Since the high sidelobe level (SLL) of OFDM waveforms degrades radar detection for weak targets, and the cross-correlation generated by other BSs further exacerbates detection performance, we devise a joint design scheme for OFDM sequence and receive filter by minimizing the integrated sidelobe level (ISL) while satisfying mainlobe level, peak-to-average power ratio (PAPR) and spectrum allocation constraints. To address this non-convex problem, we propose an alternating optimization (AO)-based algorithm. Numerical simulations validate the effectiveness of the proposed method, demonstrating the superiority of SSL reduction in the MBS system over the matched filtering method.
| null |
https://arxiv.org/abs/2506.20231v1
|
https://arxiv.org/pdf/2506.20231v1.pdf
| null |
[
"Xinghe Li",
"Kainan Cheng",
"Huiyong Li",
"Ziyang Cheng"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "To avoid the problem caused by low-frequent entity-relation pairs, our MBS uses the estimated probabilities from a trained model $\\mathbf{\\theta}'$ to calculate frequencies for each triplet and query. By using $\\mathbf{\\theta}'$, the NS loss in KGE with MBS is represented as follows:\r\n\\begin{align}\r\n &\\ell_{mbs}(\\mathbf{\\theta};\\mathbf{\\theta}') \\nonumber \\\\\r\n=&-\\frac{1}{|D|}\\sum_{(x,y) \\in D} \\Bigl[A_{mbs}(\\mathbf{\\theta}')\\log(\\sigma(s_{\\mathbf{\\theta}}(x,y)+\\gamma))\\nonumber\\\\\r\n &+\\frac{1}{\\nu}sum_{y_{i}\\sim p_n(y_{i}|x)}^{\\nu}B_{mbs}(\\mathbf{\\theta}')\\log(\\sigma(-s_{\\mathbf{\\theta}}(x,y_i)-\\gamma))\\Bigr],\r\n\\end{align}",
"full_name": "Model-based Subsampling",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Negative Sampling",
"parent": null
},
"name": "MBS",
"source_title": "Model-based Subsampling for Knowledge Graph Completion",
"source_url": "https://arxiv.org/abs/2309.09296v1"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/joint-quantization-and-pruning-neural
|
2506.20084
| null | null |
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
|
Towards fast, hardware-efficient, and low-complexity receivers, we propose a compression-aware learning approach and examine it on free-space optical (FSO) receivers for turbulence mitigation. The learning approach jointly quantize, prune, and train a convolutional neural network (CNN). In addition, we propose to have the CNN weights of power of two values so we replace the multiplication operations bit-shifting operations in every layer that has significant lower computational cost. The compression idea in the proposed approach is that the loss function is updated and both the quantization levels and the pruning limits are optimized in every epoch of training. The compressed CNN is examined for two levels of compression (1-bit and 2-bits) over different FSO systems. The numerical results show that the compression approach provides negligible decrease in performance in case of 1-bit quantization and the same performance in case of 2-bits quantization, compared to the full-precision CNNs. In general, the proposed IM/DD FSO receivers show better bit-error rate (BER) performance (without the need for channel state information (CSI)) compared to the maximum likelihood (ML) receivers that utilize imperfect CSI when the DL model is compressed whether with 1-bit or 2-bit quantization.
| null |
https://arxiv.org/abs/2506.20084v1
|
https://arxiv.org/pdf/2506.20084v1.pdf
| null |
[
"Mohanad Obeed",
"Ming Jian"
] |
[
"Quantization"
] | 2025-06-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
}
] |
https://paperswithcode.com/paper/low-complexity-ordered-reliability-direct
|
2506.20079
| null | null |
Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
|
We propose a reduced complexity approach to pattern-based soft decoding of block codes. We start from the ORDEPT decoding algorithm which tests a list of partial error patterns organized in the order of their likelihood and attempts to complete the patterns creating candidate codewords. We then propose an early termination criterion. Once a candidate codeword is found, its log-likelihood difference to the received sequence is compared to a preset threshold and the decoding decision is instantly made in case the likelihood deviation is below the threshold. We demonstrate that while keeping the same block error rate (BLER) performance, the proposed algorithm's latency and complexity is multiple times smaller than that of the state-of-the art competitors including the Chase II, ORBGRAND, GCD, and the very recent ORDEPT with Soft-Output GRAND termination which necessitates several multiplications in each query processing.
| null |
https://arxiv.org/abs/2506.20079v1
|
https://arxiv.org/pdf/2506.20079v1.pdf
| null |
[
"Reza Hadavian",
"Dmitri Truhachev"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/near-field-energy-harvesting-using-xl-mimo
|
2506.20067
| null | null |
Near-Field Energy Harvesting Using XL-MIMO Over Non-Stationary Channels
|
This paper explores the maximization of the harvested power efficiency (HPE) in a modular extremely large multiple-input multiple-output (XL-MIMO) system, which supports energy harvesting (EH) for near-field users. These users are located in spatially distinct visibility regions (VRs) with non-stationary channel characteristics. We propose to determine which sub-arrays are switched on or off as well the power control coefficients at the sub-arrays to maximize the HPE. The design can be processed via a multi-tier joint optimization framework based on fractional programming. The numerical results showcase that the HPE performance of the proposed algorithm is nearly optimal, comparable to that of exhaustive search. As a matter of fact, it achieves up to a 120% gain over the benchmark scheme which uses the entire XL-MIMO array with equal power allocation (PA) across sub-arrays, while significantly reducing the computational time.
| null |
https://arxiv.org/abs/2506.20067v1
|
https://arxiv.org/pdf/2506.20067v1.pdf
| null |
[
"Muhammad Zeeshan Mumtaz",
"Mohammadali Mohammadi",
"Hien Quoc Ngo",
"Michail Matthaiou"
] |
[] | 2025-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/near-field-swipt-using-xl-mimo-power
|
2506.20050
| null | null |
Near-Field SWIPT Using XL-MIMO: Power Allocation and Subarray Activation
|
This paper investigates the simultaneous wireless information and power transfer (SWIPT) capability of a modular extremely large multiple-input multiple-output (XL-MIMO) system, in the context of power consumption (PC) efficiency. The network users are divided into two functional categories: information decoding (ID) users and energy harvesting (EH) users. Non-stationary near-field channels are considered whilst the users are located in spatially distinct visibility regions (VRs). We formulate a two-tier joint optimization problem to minimize the PC, taking into account the power allocation (PA) for ID and EH users, along with the activation of constituent XL-MIMO subarrays. This complicated mixed-integer problem is transformed into more tractable formulations and efficient algorithms are proposed for solving them. The numerical results demonstrate that the overall PC of the XL-MIMO system for the proposed method is reduced by more than 60% in comparison to the benchmark scheme of equal PA with full subarray activation (SA) and 30% against the case of optimized PA with full SA, while satisfying the quality-of-service (QoS) constraints on both the downlink rate of the ID users and harvested energy at the EH users.
| null |
https://arxiv.org/abs/2506.20050v1
|
https://arxiv.org/pdf/2506.20050v1.pdf
| null |
[
"Muhammad Zeeshan Mumtaz",
"Mohammadali Mohammadi",
"Hien Quoc Ngo",
"Michail Matthaiou"
] |
[] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/posterior-cramer-rao-bounds-on-localization
|
2506.19957
| null | null |
Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
|
Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well established, the literature lacks performance bounds for map information. This paper derives the mapping error bound (MEB), i.e., the posterior Cram\'er-Rao lower bound on the position and orientation of specular surfaces, for RF-SLAM. In particular, we consider a very general scenario with single- and double-bounce reflections, as well as distributed anchors. We demonstrate numerically that a state-of-the-art RF-SLAM algorithm asymptotically converges to this MEB. The bounds assess not only the localization (position and orientation) but also the mapping performance of RF-SLAM algorithms in terms of global features.
| null |
https://arxiv.org/abs/2506.19957v1
|
https://arxiv.org/pdf/2506.19957v1.pdf
| null |
[
"Benjamin J. B. Deutschmann",
"Xuhong LI",
"Florian Meyer",
"Erik Leitinger"
] |
[
"Position",
"Simultaneous Localization and Mapping"
] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/revisiting-r-statistical-envelope-analysis
|
2506.19956
| null | null |
Revisiting R: Statistical Envelope Analysis for Lightweight RF Modulation Classification
|
Modulation classification plays a crucial role in wireless communication systems, enabling applications such as cognitive radio, spectrum monitoring, and electronic warfare. Conventional techniques often involve deep learning or complex feature extraction, which, while effective, require substantial computational resources and memory. An early approach by Chan and Gadbois in 1985 introduced a theoretical method for modulation classification using a mathematically derived parameter called R. The authors proved that the R value - the ratio of the variance to the square of the mean of the signal envelope - can be a distinguishing feature for classification. In this work, we revisit the R value and show that classification accuracy can be improved further through statistical methods. We extend R-value analysis to demonstrate its effectiveness even after signals are transformed using the Hilbert transform followed by the Short-Time Fourier Transform (STFT). Our analysis includes testing on 300000 signals across AM, DSB, and SSB classes, with each class having 100000 random variations. On average, we achieve 98.60, 97.30, and 97.90 percent classification accuracy for AM, DSB, and SSB signals after applying the Hilbert transform. Similar or better accuracies are observed after applying the STFT, reaching 98.80, 99.10, and 99.00 percent, respectively, for AM, DSB, and SSB types.
| null |
https://arxiv.org/abs/2506.19956v1
|
https://arxiv.org/pdf/2506.19956v1.pdf
| null |
[
"Srinivas Rahul Sapireddy",
"Mostafizur Rahman"
] |
[
"Classification"
] | 2025-06-24T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Attention Model",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Reinforcement Learning Frameworks",
"parent": null
},
"name": "AM",
"source_title": "Attention, Learn to Solve Routing Problems!",
"source_url": "http://arxiv.org/abs/1803.08475v3"
}
] |
https://paperswithcode.com/paper/beyond-200-gb-s-lane-an-analytical-approach
|
2506.19684
| null | null |
Beyond 200 Gb/s/lane: An Analytical Approach to Optimal Detection in Shaped IM-DD Optical Links with Relative Intensity Noise
|
Next-generation intensity-modulation (IM) and direct-detection (DD) systems used in data centers are expected to operate at 400 Gb/s/lane and beyond. Such rates can be achieved by increasing the system bandwidth or the modulation format, which in turn requires maintaining or increasing the signal-to-noise ratio (SNR). Such SNR requirements can be achieved by increasing the transmitted optical power. This increase in optical power causes the emergence of relative intensity noise (RIN), a signal-dependent impairment inherent to the transmitter laser, which ultimately limits the performance of the system. In this paper, we develop an analytical symbol error rate (SER) expression for the optimal detector for the IM-DD optical link under study. The developed expression takes into account the signal-dependent nature of RIN and does not make any assumptions on the geometry or probability distribution of the constellation. Our expression is therefore applicable to general probabilistically and/or geometrically shaped systems. Unlike results available in the literature, our proposed expression provides a perfect match to numerical simulations of probabilistic and geometrically shaped systems.
| null |
https://arxiv.org/abs/2506.19684v1
|
https://arxiv.org/pdf/2506.19684v1.pdf
| null |
[
"Felipe Villenas",
"Kaiquan Wu",
"Yunus Can Gültekin",
"Jamal Riani",
"Alex Alvarado"
] |
[] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-error-rate-approximations-for-fso-systems
|
2506.19627
| null | null |
On Error Rate Approximations for FSO Systems with Weak Turbulence and Pointing Errors
|
Atmospheric attenuation, atmospheric turbulence, geometric spread, and pointing errors, degrade the performance of free-space optical transmission. In the weak turbulence regime, the probability density function describing the distribution of the channel fading coefficient that models these four effects is known in the literature. This function is an integral equation, which makes it difficult to find simple analytical expressions of important performance metrics such as the bit error rate (BER) and symbol error rate (SER). In this paper, we present simple and accurate approximations of the average BER and SER for pulse-amplitude modulation (PAM) in the weak turbulence regime for an intensity modulation and direct detection system. Our numerical results show that the proposed expressions exhibit excellent accuracy when compared against Monte Carlo simulations. To demonstrate the usefulness of the developed approximations, we perform two asymptotic analyses. First, we investigate the additional transmit power required to maintain the same SER when the spectral efficiency increases by 1 bit/symbol. Second, we study the asymptotic behavior of our SER approximation for dense PAM constellations and high transmit power.
| null |
https://arxiv.org/abs/2506.19627v1
|
https://arxiv.org/pdf/2506.19627v1.pdf
| null |
[
"Carmen Álvarez Roa",
"Yunus Can Gültekin",
"Kaiquan Wu",
"Cornelis Willem Korevaar",
"Alex Alvarado"
] |
[] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-wireless-self-calibrating-ultrasound
|
2506.19612
| null | null |
A Wireless Self-Calibrating Ultrasound Microphone Array with Sub-Microsecond Synchronization
|
We present a novel system architecture for a distributed wireless, self-calibrating ultrasound microphone network for synchronized in-air acoustic sensing. Once deployed the embedded nodes determine their position in the environment using the infrared optical tracking system found in the HTC Vive Lighthouses. After self-calibration, the nodes start sampling the ultrasound microphone while embedding a synchronization signal in the data which is established using a wireless Sub-1GHz RF link. Data transmission is handled via the Wi-Fi 6 radio that is embedded in the nodes' SoC, decoupling synchronization from payload transport. A prototype system with a limited amount of network nodes was used to verify the proposed distributed microphone array's wireless data acquisition and synchronization capabilities. This architecture lays the groundwork for scalable, deployable ultrasound arrays for sound source localization applications in bio-acoustic research and industrial acoustic monitoring.
| null |
https://arxiv.org/abs/2506.19612v1
|
https://arxiv.org/pdf/2506.19612v1.pdf
| null |
[
"Dennis Laurijssen",
"Rens Baeyens",
"Walter Daems",
"Jan Steckel"
] |
[
"Sound Source Localization"
] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reconfigurable-intelligent-surfaces-for-6g-6
|
2506.19526
| null | null |
Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment
|
As the wireless research community moves toward shaping the vision of sixth-generation (6G) networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for controlling the propagation environment. Although RIS has not yet been standardized, its versatile applications and enabling capabilities have attracted growing attention in both academia and industry. This survey presents a comprehensive review of RIS technology spanning theoretical foundations, design aspects, and practical deployment considerations. In contrast to existing surveys that focus on isolated aspects, this work offers an integrated view covering use cases, control mechanisms, channel sounding methodologies, and channel estimation strategies. Each of these topics is reviewed through the lens of recent literature, synthesizing the latest advancements to provide updated insights for both academic researchers and industry practitioners. It further addresses emerging topics such as standardization activities and industrial perspectives, which are often overlooked in prior literature. By bridging theoretical insights with practical challenges, this survey aims to provide a holistic understanding of RIS and support its evolution from a research concept toward real-world implementation.
| null |
https://arxiv.org/abs/2506.19526v1
|
https://arxiv.org/pdf/2506.19526v1.pdf
| null |
[
"Prasetyo Putranto",
"Anis Amazigh Hamza",
"Sameh Mabrouki",
"Nasrullah Armi",
"Iyad Dayoub"
] |
[
"Survey"
] | 2025-06-24T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/neural-collapse-based-deep-supervised
|
2506.19476
| null | null |
Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
|
Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often observe only partial wireless environments characterized by different power delay profiles. Federated learning is capable of addressing this limited observability, but often struggles with data heterogeneity. To tackle this challenge, we propose a neural collapse (NC) inspired deep supervised federated learning (NCDSFL) algorithm.
| null |
https://arxiv.org/abs/2506.19476v1
|
https://arxiv.org/pdf/2506.19476v1.pdf
| null |
[
"Kaidi Xu",
"Shenglong Zhou",
"Geoffrey Ye Li"
] |
[
"Federated Learning"
] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/low-complexity-semantic-packet-aggregation
|
2506.19451
| null | null |
Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search
|
Tokens are fundamental processing units of generative AI (GenAI) and large language models (LLMs), and token communication (TC) is essential for enabling remote AI-generate content (AIGC) and wireless LLM applications. Unlike traditional bits, each of which is independently treated, the semantics of each token depends on its surrounding context tokens. This inter-token dependency makes TC vulnerable to outage channels, where the loss of a single token can significantly distort the original message semantics. Motivated by this, this paper focuses on optimizing token packetization to maximize the average token similarity (ATS) between the original and received token messages under outage channels. Due to inter-token dependency, this token grouping problem is combinatorial, with complexity growing exponentially with message length. To address this, we propose a novel framework of semantic packet aggregation with lookahead search (SemPA-Look), built on two core ideas. First, it introduces the residual semantic score (RSS) as a token-level surrogate for the message-level ATS, allowing robust semantic preservation even when a certain token packet is lost. Second, instead of full search, SemPA-Look applies a lookahead search-inspired algorithm that samples intra-packet token candidates without replacement (fixed depth), conditioned on inter-packet token candidates sampled with replacement (fixed width), thereby achieving linear complexity. Experiments on a remote AIGC task with the MS-COCO dataset (text captioned images) demonstrate that SemPA-Look achieves high ATS and LPIPS scores comparable to exhaustive search, while reducing computational complexity by up to 40$\times$. Compared to other linear-complexity algorithms such as the genetic algorithm (GA), SemPA-Look achieves 10$\times$ lower complexity, demonstrating its practicality for remote AIGC and other TC applications.
| null |
https://arxiv.org/abs/2506.19451v1
|
https://arxiv.org/pdf/2506.19451v1.pdf
| null |
[
"Seunghun Lee",
"Jihong Park",
"Jinho Choi",
"HyunCheol Park"
] |
[] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/holographic-communication-via-recordable-and
|
2506.19376
| null | null |
Holographic Communication via Recordable and Reconfigurable Metasurface
|
Holographic surface based communication technologies are anticipated to play a significant role in the next generation of wireless networks. The existing reconfigurable holographic surface (RHS)-based scheme only utilizes the reconstruction process of the holographic principle for beamforming, where the channel sate information (CSI) is needed. However, channel estimation for CSI acquirement is a challenging task in metasurface based communications. In this study, inspired by both the recording and reconstruction processes of holography, we develop a novel holographic communication scheme by introducing recordable and reconfigurable metasurfaces (RRMs), where channel estimation is not needed thanks to the recording process. Then we analyze the input-output mutual information of the RRM-based communication system and compare it with the existing RHS based system. Our results show that, without channel estimation, the proposed scheme achieves performance comparable to that of the RHS scheme with perfect CSI, suggesting a promising alternative for future wireless communication networks.
| null |
https://arxiv.org/abs/2506.19376v1
|
https://arxiv.org/pdf/2506.19376v1.pdf
| null |
[
"Jinzhe Wang",
"Qinghua Guo",
"Xiaojun Yuan"
] |
[] | 2025-06-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/from-high-snr-radar-signal-to-ecg-a-transfer
|
2506.19358
| null | null |
From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data
|
Electrocardiogram (ECG), as a crucial find-grained cardiac feature, has been successfully recovered from radar signals in the literature, but the performance heavily relies on the high-quality radar signal and numerous radar-ECG pairs for training, restricting the applications in new scenarios due to data scarcity. Therefore, this work will focus on radar-based ECG recovery in new scenarios with limited data and propose a cardio-focusing and -tracking (CFT) algorithm to precisely track the cardiac location to ensure an efficient acquisition of high-quality radar signals. Furthermore, a transfer learning model (RFcardi) is proposed to extract cardio-related information from the radar signal without ECG ground truth based on the intrinsic sparsity of cardiac features, and only a few synchronous radar-ECG pairs are required to fine-tune the pre-trained model for the ECG recovery. The experimental results reveal that the proposed CFT can dynamically identify the cardiac location, and the RFcardi model can effectively generate faithful ECG recoveries after using a small number of radar-ECG pairs for training. The code and dataset are available after the publication.
| null |
https://arxiv.org/abs/2506.19358v1
|
https://arxiv.org/pdf/2506.19358v1.pdf
| null |
[
"Yuanyuan Zhang",
"Haocheng Zhao",
"Sijie Xiong",
"Rui Yang",
"Eng Gee Lim",
"Yutao Yue"
] |
[
"Transfer Learning"
] | 2025-06-24T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/a-new-pathway-to-integrated-learning-and
|
2506.18432
| null | null |
A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication
|
The rapid evolution of forthcoming sixth-generation (6G) wireless networks necessitates the seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both efficient communication and robust learning performance. This dual requirement calls for a unified framework of integrated learning and communication (ILAC), where AI enhances communication through intelligent signal processing and adaptive resource management, while wireless networks support AI model deployment by enabling efficient and reliable data exchanges. However, achieving this integration presents significant challenges in practice. Communication constraints, such as limited bandwidth and fluctuating channels, hinder learning accuracy and convergence. Simultaneously, AI-driven learning dynamics, including model updates and task-driven inference, introduce excessive burdens on communication systems, necessitating flexible, context-aware transmission strategies. Finally, we present a case study on a cost-to-performance optimization problem, where task assignments, model size selection, bandwidth allocation, and transmission power control are jointly optimized, considering computational cost, communication efficiency, and inference accuracy. Leveraging the Dinkelbach and alternating optimization algorithms, we offer a practical and effective solution to achieve an optimal balance between learning performance and communication constraints.
| null |
https://arxiv.org/abs/2506.18432v2
|
https://arxiv.org/pdf/2506.18432v2.pdf
| null |
[
"Wei Xu",
"Zhaohui Yang",
"Derrick Wing Kwan Ng",
"Robert Schober",
"H. Vincent Poor",
"Zhaoyang Zhang",
"Xiaohu You"
] |
[] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/eeg-foundation-challenge-from-cross-task-to
|
2506.19141
| null | null |
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
|
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 child to young adult subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research.
| null |
https://arxiv.org/abs/2506.19141v1
|
https://arxiv.org/pdf/2506.19141v1.pdf
| null |
[
"Bruno Aristimunha",
"Dung Truong",
"Pierre Guetschel",
"Seyed Yahya Shirazi",
"Isabelle Guyon",
"Alexandre R. Franco",
"Michael P. Milham",
"Aviv Dotan",
"Scott Makeig",
"Alexandre Gramfort",
"Jean-Remi King",
"Marie-Constance Corsi",
"Pedro A. Valdés-Sosa",
"Amit Majumdar",
"Alan Evans",
"Terrence J Sejnowski",
"Oren Shriki",
"Sylvain Chevallier",
"Arnaud Delorme"
] |
[
"EEG",
"Eeg Decoding",
"Electroencephalogram (EEG)"
] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/achieving-70-gb-s-over-a-vcsel-based-optical
|
2506.18864
| null | null |
Achieving 70 Gb/s Over A VCSEL-Based Optical Wireless Link Using A Multi-Mode Fiber-Coupled Receiver
|
In this paper, we demonstrate a laser-based optical wireless communication (OWC) system employing a 940 nm single-mode (SM) vertical cavity surface emitting laser (VCSEL) and a multi-mode (MM) fiber-coupled receiver, achieving a record data rate beyond 70 Gb/s, while the optical transmit power is below 5 mW. The use of a high speed fiber-optic photoreceiver avoids limiting the communication bandwidth by the receiver, enabling ultra-high capacity and energy-efficient light fidelity (LiFi) links to unlock new applications. This work experimentally validates the feasibility of ultra-high speed indoor OWC systems using a single low-power and low-cost VCSEL for next-generation LiFi connectivity.
| null |
https://arxiv.org/abs/2506.18864v1
|
https://arxiv.org/pdf/2506.18864v1.pdf
| null |
[
"Hossein Kazemi",
"Isaac N. O. Osahon",
"Nikolay Ledentsov Jr.",
"Ilya Titkov",
"Nikolay Ledentsov",
"Harald Haas"
] |
[] | 2025-06-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/variational-bayesian-channel-estimation-and
|
2506.18863
| null | null |
Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Links
|
We study the joint channel estimation and data detection (JED) problem in a cell-free massive multiple-input multiple-output (CF-mMIMO) network, where access points (APs) communicate with a central processing unit (CPU) over fronthaul links. However, the bandwidth of these links is limited, and thus, presents challenges to the applicability of CF-mMIMO, especially with an ever-increasing number of users. To address this, we propose a method based on variational Bayesian (VB) inference for performing the JED process, where the APs forward low-resolution quantized versions of the signals to the CPU. We consider two approaches: \emph{quantization-and-estimation} (Q-E) and \emph{estimation-and-quantization} (E-Q). In the Q-E approach, each AP uses a low-bit quantizer to quantize the signal before forwarding it to the CPU, while in the E-Q approach, each AP first performs local channel estimation and then sends a low-bit quantized version of the estimated channel to the CPU. We evaluate the performance of our VB-based approach under perfect fronthaul link (PFL) with unquantized received signals, Q-E, and E-Q in terms of symbol error rate (SER), normalized mean square error (NMSE) of the channel estimation, computational complexity, and fronthaul signaling overhead. We also compare these results with those of the linear minimum mean squared error (LMMSE) method under the PFL scenario. Our numerical results show that both the VB(Q-E) and VB(E-Q) approaches achieve superior performance compared to LMMSE(PFL), benefiting from the nonlinear modeling inherent in VB. Furthermore, the VB(Q-E) method outperforms VB(E-Q) due to errors in the local channel estimation process at the APs within the VB(E-Q) approach.
| null |
https://arxiv.org/abs/2506.18863v1
|
https://arxiv.org/pdf/2506.18863v1.pdf
| null |
[
"Sajjad Nassirpour",
"Toan-Van Nguyen",
"Hien Q. Ngo",
"Le-Nam Tran",
"Tharmalingam Ratnarajah",
"Duy H. N. Nguyen"
] |
[
"CPU",
"Quantization"
] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-state-augmented-learning-for-wireless
|
2506.18748
| null | null |
Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
|
We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous dual subgradient methods by representing the network configurations (or states) as graphs and viewing dual variables as dynamic inputs to the model, viewed as graph signals supported over the graphs. Lagrangian maximizing state-augmented policies are learned during the offline training phase, and the dual variables evolve through gradient updates while executing the learned state-augmented policies during the inference phase. Our main contributions are to illustrate how near-optimal initialization of dual multipliers for faster inference can be accomplished with dual variable regression, leveraging a secondary GNN parametrization, and how maximization of the Lagrangian over the multipliers sampled from the dual descent dynamics substantially improves the training of state-augmented models. We demonstrate the superior performance of the proposed algorithm with extensive numerical experiments in a case study of transmit power control. Finally, we prove a convergence result and an exponential probability bound on the excursions of the dual function (iterate) optimality gaps.
| null |
https://arxiv.org/abs/2506.18748v1
|
https://arxiv.org/pdf/2506.18748v1.pdf
| null |
[
"Yigit Berkay Uslu",
"Navid Naderializadeh",
"Mark Eisen",
"Alejandro Ribeiro"
] |
[
"Graph Neural Network"
] | 2025-06-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Graph Neural Network",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Graph Neural Network",
"source_title": "Graph Neural Networks: A Review of Methods and Applications",
"source_url": "https://arxiv.org/abs/1812.08434v6"
}
] |
https://paperswithcode.com/paper/on-designing-modulation-for-over-the-air
|
2506.16208
| null | null |
On Designing Modulation for Over-the-Air Computation -- Part II: Pyramid Sampling
|
Over-the-air computation (OAC) harnesses the natural superposition of wireless signals to compute aggregate functions during transmission, thereby collapsing communication and computation into a single step and significantly reducing latency and resource usage. In Part I, digital OAC was formulated as a noise-aware constellation design problem by casting encoder design as a max-min optimization that aligns minimum Euclidean distances between superimposed constellation points with squared differences of their corresponding function outputs. In this paper, Part II, we address the prohibitive complexity and quantization challenges inherent in digital OAC constellation design for large-scale edge networks. More precisely, we introduce a pyramid sampling strategy that judiciously selects a subset of superimposed constellation points to reduce the encoder design complexity from $\mathcal{O}(q^K)$ to $\mathcal{O}(q^{K-p+1})$, where $p\in\{1,\dots, K\}$ denotes the sampling order, $q$ levels of modulation, and $K$ denotes the number nodes in the network. Under the assumption of symmetric aggregation, this approach enables a controlled trade-off between computational complexity and function computation accuracy. As a special case, we propose majority-based sampling ($p=K$), which confines aggregation to only $q$ consensus points, inherently avoiding destructive overlaps and permitting the use of standard digital modulations (e.g., QAM, PSK, ASK) without bespoke constellation designs. We also show via several simulations, across various aggregation functions, modulation levels, and noise levels, that moderate sampling orders attain acceptable performance with orders-of-magnitude fewer constraints than exhaustive designs.
| null |
https://arxiv.org/abs/2506.16208v2
|
https://arxiv.org/pdf/2506.16208v2.pdf
| null |
[
"Saeed Razavikia",
"Carlo Fischione"
] |
[] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/trustworthy-prediction-with-gaussian-process
|
2506.18630
| null | null |
Trustworthy Prediction with Gaussian Process Knowledge Scores
|
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In this paper, we propose a knowledge score for predictions from Gaussian process regression (GPR) models that quantifies the extent to which observing data have reduced our uncertainty about a prediction. The knowledge score is interpretable and naturally bounded between 0 and 1. We demonstrate in several experiments that the knowledge score can anticipate when predictions from a GPR model are accurate, and that this anticipation improves performance in tasks such as anomaly detection, extrapolation, and missing data imputation. Source code for this project is available online at https://github.com/KurtButler/GP-knowledge.
|
We demonstrate in several experiments that the knowledge score can anticipate when predictions from a GPR model are accurate, and that this anticipation improves performance in tasks such as anomaly detection, extrapolation, and missing data imputation.
|
https://arxiv.org/abs/2506.18630v1
|
https://arxiv.org/pdf/2506.18630v1.pdf
| null |
[
"Kurt Butler",
"Guanchao Feng",
"Tong Chen",
"Petar Djuric"
] |
[
"Anomaly Detection",
"GPR",
"Imputation",
"Prediction"
] | 2025-06-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/on-designing-modulation-for-over-the-air-1
|
2506.15950
| null | null |
On Designing Modulation for Over-the-Air Computation -- Part I: Noise-Aware Design
|
Over-the-air computation (OAC) leverages the physical superposition property of wireless multiple access channels (MACs) to compute functions while communication occurs, enabling scalable and low-latency processing in distributed networks. While analog OAC methods suffer from noise sensitivity and hardware constraints, existing digital approaches are often limited in design complexity, which may hinder scalability and fail to exploit spectral efficiency fully. This two-part paper revisits and extends the ChannelComp framework, a general methodology for computing arbitrary finite-valued functions using digital modulation. In Part I, we develop a novel constellation design approach that is aware of the noise distribution and formulates the encoder design as a max-min optimization problem using noise-tailored distance metrics. Our design supports noise models, including Gaussian, Laplace, and heavy-tailed distributions. We further demonstrate that, for heavy-tailed noise, the optimal ChannelComp setup coincides with the solution to the corresponding max-min criterion for the channel noise with heavy-tailed distributions. Numerical experiments confirm that our noise-aware design achieves a substantially lower mean-square error than leading digital OAC methods over noisy MACs. In Part II, we consider a constellation design with a quantization-based sampling scheme to enhance modulation scalability and computational accuracy for large-scale digital OAC.
| null |
https://arxiv.org/abs/2506.15950v2
|
https://arxiv.org/pdf/2506.15950v2.pdf
| null |
[
"Saeed Razavikia",
"Carlo Fischione"
] |
[
"Low-latency processing",
"Quantization"
] | 2025-06-19T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "We propose to theoretically and empirically examine the effect of incorporating weighting schemes into walk-aggregating GNNs. To this end, we propose a simple, interpretable, and end-to-end supervised GNN model, called AWARE (Attentive Walk-Aggregating GRaph Neural NEtwork), for graph-level prediction. AWARE aggregates the walk information by means of weighting schemes at distinct levels (vertex-, walk-, and graph-level) in a principled manner. By virtue of the incorporated weighting schemes at these different levels, AWARE can emphasize the information important for prediction while diminishing the irrelevant ones—leading to representations that can improve learning performance.",
"full_name": "Attentive Walk-Aggregating Graph Neural Network",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "AWARE",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/sizing-antenna-arrays-for-near-field
|
2506.18465
| null | null |
Sizing Antenna Arrays for Near-field Communication and Sensing
|
This paper presents key performance metrics for near-field communication and sensing systems with a focus on their scaling behavior as a function of the antenna array aperture. Analytical expressions are derived for several standard array geometries to enable the design of the large antenna arrays for given system requirements. First, the near-field beam focusing is analyzed and the minimum beamdepth is observed to rapidly saturate to a low asymptotic limit as the array aperture increases. In contrast, the near-field region span is shown to scale quadratically with the array aperture. Based on these two metrics, the maximum number of resolvable beamspots at 3 dB separation is derived analytically, exhibiting a linear dependence on the array aperture. Finally, the number of significant singular values of a channel observed at the array's broadside is estimated, showing a power-law dependence on the aperture. The resulting expressions provide practical design guidelines for evaluating aperture requirements in near-field communication and sensing applications.
| null |
https://arxiv.org/abs/2506.18465v1
|
https://arxiv.org/pdf/2506.18465v1.pdf
| null |
[
"Marcin Wachowiak",
"André Bourdoux",
"Sofie Pollin"
] |
[] | 2025-06-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/generative-diffusion-receivers-achieving
|
2506.18419
| null | null |
Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications
|
This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers. Traditional wireless receivers have relied on mathematical modeling and Bayesian inference, achieving remarkable success in most areas but falling short in their ability to characterize channel matrices. Neural networks (NNs) have demonstrated significant potential in this aspect. Nevertheless, integrating traditional inference methods with NNs presents challenges, particularly in tracking the error progression. Given the inevitable presence of noise in wireless systems, generative models that are more resilient to noise are garnering increased attention. In this paper, we propose re-evaluating the MIMO-OFDM receiver using diffusion models, which is a common generative approach. With diffusion models, we can effectively leverage prior knowledge of channel matrices and incorporate traditional signal estimation components. Specifically, we explore the diffusion system and introduce an imagination-screening strategy to guide the diffusion process. Furthermore, diffusion models enable adaptation to varying noise levels and pilot schemes using the same NN, significantly reducing training and deployment costs. Simulated results reveal that, for pilot densities ranging from 4-6 pilots per 64-subcarrier block and signal-to-noise ratios (SNRs) from -4 dB to 0 dB, our proposed receiver reduces channel-reconstruction error by up to two times compared to leading deep-learning models, with the most pronounced improvements observed in low-pilot conditions. Additionally, performance enhancements can be achieved with a larger imagination size, despite increased computational complexity.
|
This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers.
|
https://arxiv.org/abs/2506.18419v1
|
https://arxiv.org/pdf/2506.18419v1.pdf
| null |
[
"Yuzhi Yang",
"Omar Alhussein",
"Atefeh Arani",
"Zhaoyang Zhang",
"Mérouane Debbah"
] |
[
"Bayesian Inference"
] | 2025-06-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/llm-integrated-digital-twins-for-hierarchical
|
2506.18293
| null | null |
LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
|
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual reasoning capabilities. This article proposes LLM-driven DTs for network optimization (LLM-DTNet), a hierarchical framework that integrates multi-layer DT architectures with LLM-based orchestration to enable adaptive, real-time RRM in heterogeneous NextG networks. We present the fundamentals and design considerations of LLM-DTNet while discussing its effectiveness in proactive and situation-aware network management across terrestrial and non-terrestrial applications. Furthermore, we highlight key challenges, including scalable DT modeling, secure LLM-DT integration, energy-efficient implementations, and multimodal data processing, shaping future advancements in NextG intelligent wireless networks.
| null |
https://arxiv.org/abs/2506.18293v1
|
https://arxiv.org/pdf/2506.18293v1.pdf
| null |
[
"Majumder Haider",
"Imtiaz Ahmed",
"Zoheb Hassan",
"Kamrul Hasan",
"H. Vincent Poor"
] |
[
"Decision Making",
"Management"
] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multimodal-visual-image-based-user
|
2506.18218
| null | null |
Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks
|
This paper proposes an approach that leverages multimodal data by integrating visual images with radio frequency (RF) pilots to optimize user association and beamforming in a downlink wireless cellular network under a max-min fairness criterion. Traditional methods typically optimize wireless system parameters based on channel state information (CSI). However, obtaining accurate CSI requires extensive pilot transmissions, which lead to increased overhead and latency. Moreover, the optimization of user association and beamforming is a discrete and non-convex optimization problem, which is challenging to solve analytically. In this paper, we propose to incorporate visual camera data in addition to the RF pilots to perform the joint optimization of user association and beamforming. The visual image data helps enhance channel awareness, thereby reducing the dependency on extensive pilot transmissions for system optimization. We employ a learning-based approach based on using first a detection neural network that estimates user locations from images, and subsequently two graph neural networks (GNNs) that extract features for system optimization based on the location information and the received pilots, respectively. Then, a multimodal GNN is constructed to integrate the features for the joint optimization user association and beamforming. Simulation results demonstrate that the proposed method achieves superior performance, while having low computational complexity and being interpretable and generalizable, making it an effective solution as compared to traditional methods based only on RF pilots.
| null |
https://arxiv.org/abs/2506.18218v1
|
https://arxiv.org/pdf/2506.18218v1.pdf
| null |
[
"Yinghan Li",
"Yiming Liu",
"Wei Yu"
] |
[
"Fairness"
] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bayesian-multiobject-tracking-with-neural
|
2506.18124
| null | null |
Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models
|
Multiobject tracking (MOT) is an important task in applications including autonomous driving, ocean sciences, and aerospace surveillance. Traditional MOT methods are model-based and combine sequential Bayesian estimation with data association and an object birth model. More recent methods are fully data-driven and rely on the training of neural networks. Both approaches offer distinct advantages in specific settings. In particular, model-based methods are generally applicable across a wide range of scenarios, whereas data-driven MOT achieves superior performance in scenarios where abundant labeled data for training is available. A natural thought is whether a general framework can integrate the two approaches. This paper introduces a hybrid method that utilizes neural networks to enhance specific aspects of the statistical model in Bayesian MOT that have been identified as overly simplistic. By doing so, the performance of the prediction and update steps of Bayesian MOT is improved. To ensure tractable computation, our framework uses belief propagation to avoid high-dimensional operations combined with sequential Monte Carlo methods to perform low-dimensional operations efficiently. The resulting method combines the flexibility and robustness of model-based approaches with the capability to learn complex information from data of neural networks. We evaluate the performance of the proposed method based on the nuScenes autonomous driving dataset and demonstrate that it has state-of-the-art performance
| null |
https://arxiv.org/abs/2506.18124v1
|
https://arxiv.org/pdf/2506.18124v1.pdf
| null |
[
"Shaoxiu Wei",
"Mingchao Liang",
"Florian Meyer"
] |
[
"Autonomous Driving"
] | 2025-06-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/near-field-propagation-and-spatial-non
|
2506.17887
| null | null |
Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G
|
Next generation cellular deployments are expected to exploit the 6-24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies render the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models invalid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. Finally, a simulation framework for near-field and SNS is developed within the structure of the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compared to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.
| null |
https://arxiv.org/abs/2506.17887v1
|
https://arxiv.org/pdf/2506.17887v1.pdf
| null |
[
"Huixin Xu",
"Jianhua Zhang",
"Pan Tang",
"Hongbo Xing",
"Haiyang Miao",
"Nan Zhang",
"Jian Li",
"Jianming Wu",
"Wenfei Yang",
"Zhening Zhang",
"Wei Jiang",
"Zijian He",
"Afshin Haghighat",
"Qixing Wang",
"Guangyi Liu"
] |
[] | 2025-06-22T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/machine-learning-based-near-field
|
2506.17810
| null | null |
Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
|
The conventional MUltiple SIgnal Classification (MUSIC) algorithm is effective for angle-of-arrival estimation in the far-field and can be extended for full source localization in the near-field. However, it suffers from high computational complexity, which becomes especially prohibitive in near-field scenarios due to the need for exhaustive 3D grid searches. This paper presents a machine learning-based approach for 3D localization of near-field sources in mixed line-of-sight (LoS)/non-LoS scenarios. A convolutional neural network (CNN) learns the mapping between the eigenvectors of the received signal's covariance matrix at the anchor node and the sources' 3D locations. The detailed description of the proposed CNN model is provided. The effectiveness and time efficiency of the proposed CNN-based localization approach is corroborated via numerical simulations.
| null |
https://arxiv.org/abs/2506.17810v1
|
https://arxiv.org/pdf/2506.17810v1.pdf
| null |
[
"Parisa Ramezani",
"Seyed Jalaleddin Mousavirad",
"Mattias O'Nils",
"Emil Björnson"
] |
[] | 2025-06-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rethinking-the-role-of-operating-conditions
|
2506.17740
| null | null |
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
|
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these methods, domain generalization approaches can handle complex scenarios by extracting condition-invariant fault features. Although many studies have considered fault diagnosis in specific multi-condition scenarios, the extent to which operating conditions affect fault information has been scarcely studied, which is crucial. However, the extent to which operating conditions affect fault information has been scarcely studied, which is crucial. When operating conditions have a significant impact on fault features, directly applying domain generalization methods may lead the model to learn condition-specific information, thereby reducing its overall generalization ability. This paper investigates the performance of existing end-to-end domain generalization methods under varying conditions, specifically in variable-speed and variable-load scenarios, using multiple experiments on a real-world gearbox. Additionally, a two-stage diagnostic framework is proposed, aiming to improve fault diagnosis performance under scenarios with significant operating condition impacts. By incorporating a domain-generalized encoder with a retraining strategy, the framework is able to extract condition-invariant fault features while simultaneously alleviating potential overfitting to the source domain. Several experiments on a real-world gearbox dataset are conducted to validate the effectiveness of the proposed approach.
| null |
https://arxiv.org/abs/2506.17740v1
|
https://arxiv.org/pdf/2506.17740v1.pdf
| null |
[
"Pengyu Han",
"Zeyi Liu",
"Shijin Chen",
"Dongliang Zou",
"Xiao He"
] |
[
"Diagnostic",
"Domain Generalization",
"Fault Diagnosis",
"Transfer Learning"
] | 2025-06-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/two-stage-prony-based-estimation-of
|
2506.17599
| null | null |
Two-Stage Prony-Based Estimation of Fractional Delay and Doppler Shifts in OTFS Modulation
|
This paper addresses the estimation of fractional delay and Doppler shifts in multipath channels that cause doubly selective fading-an essential task for integrated sensing and communication (ISAC) systems in high-mobility environments. Orthogonal Time Frequency Space (OTFS) modulation enables simple and robust channel compensation under such conditions. However, fractional delay and Doppler components introduce inter-path interference, degrading estimation accuracy. We propose a two-stage estimation method based on Prony's technique using OTFS pilot signals with M subchannels and N pilot repetitions. In the first stage, Doppler frequencies are estimated by jointly solving M coupled Prony equations, exploiting the periodicity of the pilot signal. In the second stage, delays are estimated by applying the discrete Fourier transform (DFT) and Prony's method to each Doppler component obtained in the first stage. The proposed method can accurately estimate up to N-1 delay-Doppler parameters under noiseless conditions. In noisy environments, conventional information criteria such as AIC and BIC yield suboptimal performance; thus, a heuristic model order selection is adopted. Numerical simulations confirm that the proposed method achieves high estimation accuracy, highlighting its potential for future ISAC frameworks.
| null |
https://arxiv.org/abs/2506.17599v1
|
https://arxiv.org/pdf/2506.17599v1.pdf
| null |
[
"Yutaka Jitsumatsu",
"Liangchen Sun"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-06-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-implementation-of-multi-sensor
|
2506.17205
| null | null |
Efficient Implementation of Multi-sensor Adaptive Birth Samplers for Labeled Random Finite Set Tracking
|
Adaptive track initiation remains a crucial component of many modern multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A naive construction of this adaptive birth set density results in an exponential number of newborn components in the number of sensors. A truncation procedure was provided that leverages a Gibbs sampler to truncate the birth density, reducing the complexity to quadratic in the number of sensors. However, only a limited discussion has been provided on additional algorithmic techniques that can be employed to substantially reduce the complexity in practical tracking applications. In this paper, we propose five efficiency enhancements for the labeled random finite sets multi-sensor adaptive birth procedure. Simulation results are provided to demonstrate their computational benefits and show that they result in a negligible change to the multi-target tracking performance.
| null |
https://arxiv.org/abs/2506.17205v1
|
https://arxiv.org/pdf/2506.17205v1.pdf
| null |
[
"Jennifer Bondarchuk",
"Anthony Trezza",
"Donald J. Bucci Jr"
] |
[] | 2025-06-20T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/intelligent-reflecting-surfaces-for-thz
|
2506.17200
| null | null |
Intelligent Reflecting Surfaces for THz Communications: Fundamentals, Key Solutions, and System Prototyping
|
Intelligent reflecting surfaces (IRSs) have emerged as a cost-effective technology for terahertz (THz) communications by enabling programmable control of the wireless environment. This paper provides a comprehensive overview of IRSs-aided THz communications, covering hardware designs, advanced signal processing techniques, and practical deployment strategies. It first examines key THz reconfigurable metasurface architectures, including electronic, optical, phase-change material, and micro-electromechanical systems (MEMS)-based implementations, highlighting their reconfiguration mechanisms and challenges. Then, fundamental effects including near field and beam squint in wideband THz systems are analyzed, along with their impacts on system performance. The paper further explores conventional and beam-squint-assisted channel estimation methods, innovative beam management strategies, and deployment considerations across large- and small-scale scenarios. Practical experiments at 220 gigahertz (GHz) validate the effectiveness of IRS in improving signal strength and communication reliability for both single-user and multi-user setups.
| null |
https://arxiv.org/abs/2506.17200v1
|
https://arxiv.org/pdf/2506.17200v1.pdf
| null |
[
"Qingqing Wu",
"Yanze Zhu",
"Qiaoyan Peng",
"Wanming Hao",
"Yanzhao Hou",
"Fengyuan Yang",
"Wencai Yan",
"Guoning Wang",
"Wen Chen",
"Chi Qiu"
] |
[] | 2025-06-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-energy-efficient-passive-beamforming
|
2506.17189
| null | null |
On Energy-Efficient Passive Beamforming Design of RIS-Assisted CoMP-NOMA Networks
|
This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancement-only PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RIS-assisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.
| null |
https://arxiv.org/abs/2506.17189v1
|
https://arxiv.org/pdf/2506.17189v1.pdf
| null |
[
"Muhammad Umer",
"Muhammad Ahmed Mohsin",
"Aamir Mahmood",
"Haejoon Jung",
"Haris Pervaiz",
"Mikael Gidlund",
"Syed Ali Hassan"
] |
[] | 2025-06-20T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/moric-csi-delay-doppler-decomposition-for
|
2506.12997
| null | null |
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
|
The newly established IEEE 802.11bf Task Group aims to amend the WLAN standard to support advanced sensing applications such as human activity recognition (HAR). Although studies have demonstrated the potential of sub-7 GHz Wi-Fi Channel State Information (CSI) for HAR, no method currently performs reliably in real-world scenarios. This work tackles the poor generalization of Wi-Fi-based HAR by introducing an innovative approach to extracting and utilizing movement-related representations, which makes it robust to noise and static environmental properties. This is achieved by transforming CSI signals into the delay profile space and decomposing them into various Doppler velocities, which serve as informative projections of a mobile point's velocity from different unknown random angles. To mitigate the impact of this randomness, MORIC is introduced as a novel time series classification model based on random convolutional kernels, designed to be invariant to the random order and repetition of input representations, thereby enabling robust Wi-Fi CSI-based activity classification. Experimental results on the collected dataset demonstrate that the proposed method outperforms state-of-the-art approaches in terms of generalization accuracy for hand motion recognition, particularly for challenging gestures. Furthermore, incorporating a small number of calibration samples leads to a significant improvement in accuracy, enhancing the practicality of the method for real-world deployment.
| null |
https://arxiv.org/abs/2506.12997v2
|
https://arxiv.org/pdf/2506.12997v2.pdf
| null |
[
"Navid Hasanzadeh",
"Shahrokh Valaee"
] |
[
"Activity Recognition",
"Human Activity Recognition",
"Time Series Classification"
] | 2025-06-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/low-complexity-receiver-design-for-affine
|
2506.17010
| null | null |
Low-Complexity Receiver Design for Affine Filter Bank Modulation
|
We propose a low-complexity receiver structure for the recently introduced Affine Filter Bank Modulation (AFBM) scheme, which is a novel waveform designed for integrated sensing and communications (ISAC) systems operating in doubly-dispersive (DD) channels. The proposed receiver structure is based on the Gaussian Belief Propagation (GaBP) framework, making use of only element-wise scalar operations to perform detection of the transmitted symbols. Simulation results demonstrate that AFBM in conjunction with GaBP outperforms affine frequency division multiplexing (AFDM) in terms of bit error rates (BERs) in DD channels, while achieving very low out-of-band emissions (OOBE) in high-mobility scenarios.
| null |
https://arxiv.org/abs/2506.17010v1
|
https://arxiv.org/pdf/2506.17010v1.pdf
| null |
[
"Kuranage Roche Rayan Ranasinghe",
"Bruno S. Chang",
"Giuseppe Thadeu Freitas de Abreu"
] |
[
"ISAC"
] | 2025-06-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/wi-fi-sensing-tool-release-gathering-802-11ax
|
2506.16957
| null | null |
Wi-Fi Sensing Tool Release: Gathering 802.11ax Channel State Information from a Commercial Wi-Fi Access Point
|
Wi-Fi sensing has emerged as a powerful technology, leveraging channel state information (CSI) extracted from wireless data packets to enable diverse applications, ranging from human presence detection to gesture recognition and health monitoring. However, CSI extraction from commercial Wi-Fi access point lacks and out of date. This paper introduces ZTECSITool,a toolkit designed to capture high-resolution CSI measurements from commercial Wi-Fi 6 (802.11ax) access points, supporting bandwidths up to 160 MHz and 512 subcarriers. ZTECSITool bridges a critical gap in Wi-Fi sensing research, facilitating the development of next-generation sensing systems. The toolkit includes customized firmware and open-source software tools for configuring, collecting, and parsing CSI data, offering researchers a robust platform for advanced sensing applications. We detail the command protocols for CSI extraction, including band selection,STA filtering, and report configuration, and provide insights into the data structure of the reported CSI. Additionally, we present a Python-based graphical interface for real-time CSI visualization and analysis
| null |
https://arxiv.org/abs/2506.16957v1
|
https://arxiv.org/pdf/2506.16957v1.pdf
| null |
[
"Zisheng Wang",
"Feng Li",
"Hangbin Zhao",
"Zihuan Mao",
"Yaodong Zhang",
"Qisheng Huang",
"Bo Cao",
"Mingming Cao",
"Baolin He",
"Qilin Hou"
] |
[
"Gesture Recognition"
] | 2025-06-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/beamforming-design-for-minimizing-the-signal
|
2506.16767
| null | null |
Beamforming design for minimizing the signal power estimation error
|
We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon beamformer and the minimum mean squared error (MMSE) beamformer. The Capon beamformer, also known as the minimum power distortionless response (MPDR) or the minimum variance distortionless response (MVDR) beamformer, is a widely used method in array signal processing. A curious feature of both the Capon and the MMSE beamformers is their tendency to either overestimate or underestimate the signal power. That is, they are not asymptotically unbiased (as the sample size approaches infinity). To address this issue, we propose to shrink the Capon beamformer by finding a scaling factor that minimizes the mean squared error (MSE) of the signal power estimate. The new beamformer, referred to as the Capon$^+$ beamformer, is evaluated against the Capon and MMSE beamformers in terms of bias, signal power MSE, and signal waveform MSE. The Capon$^+$ beamformer strikes a better balance between signal power and waveform estimation while also exhibiting minimal bias, which approaches zero as the sample size increases.
| null |
https://arxiv.org/abs/2506.16767v1
|
https://arxiv.org/pdf/2506.16767v1.pdf
| null |
[
"Esa Ollila",
"Xavier Mestre",
"Elias Raninen"
] |
[] | 2025-06-20T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/manifold-learning-for-personalized-and-label
|
2506.16494
| null | null |
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
|
Electrocardiograms (ECGs) provide direct, non-invasive measurements of heart activity and are well-established tools for detecting and monitoring cardiovascular disease. However, manual ECG analysis can be time-consuming and prone to errors. Machine learning has emerged as a promising approach for automated heartbeat recognition and classification, but substantial variations in ECG signals make it challenging to develop generalizable models. ECG signals can vary widely across individuals and leads, while datasets often follow different labeling standards and may be biased, all of which greatly hinder supervised methods. Conventional unsupervised methods, e.g. principal component analysis, prioritize large (and often obvious) variances in the data and typically overlook subtle yet clinically relevant patterns. If labels are missing and/or variations are significant but small, both approaches fail. Here, we show that nonlinear dimensionality reduction (NLDR) can accommodate these issues and identify medically relevant features in ECG signals, with no need for training or prior information. Using the MLII and V1 leads of the MIT-BIH dataset, we demonstrate that t-distributed stochastic neighbor embedding and uniform manifold approximation and projection can discriminate individual recordings in mixed populations with >= 90% accuracy and distinguish different arrhythmias in individual patients with a median accuracy of 98.96% and a median F1-score of 91.02%. The results show that NLDR holds much promise for cardiac monitoring, including the limiting cases of single-lead ECG and the current 12-lead standard of care, and for personalized health care beyond cardiology.
| null |
https://arxiv.org/abs/2506.16494v1
|
https://arxiv.org/pdf/2506.16494v1.pdf
| null |
[
"Amir Reza Vazifeh",
"Jason W. Fleischer"
] |
[
"Dimensionality Reduction"
] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/widely-linear-augmented-extreme-learning
|
2506.14557
| null | null |
Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
|
Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Although CELMAH shares structural similarities with WLP, it employs a different core algorithm and does not fully exploit the signal impropriety. By incorporating WLP principles, we derive a tailored formulation suited to the network structure and propose the CELM augmented by widely linear least squares (CELM-WLLS) for post-distortion. The proposed approach offers enhanced communication robustness and is highly effective for satellite communication scenarios characterized by dynamic channel conditions and non-linear impairments. CELM-WLLS is designed to improve signal recovery performance and outperform traditional methods such as least square (LS) and minimum mean square error (MMSE). Compared to CELMAH, CELM-WLLS demonstrates approximately 0.8 dB gain in BER performance, and also achieves a two-thirds reduction in computational complexity, making it a more efficient solution.
| null |
https://arxiv.org/abs/2506.14557v2
|
https://arxiv.org/pdf/2506.14557v2.pdf
| null |
[
"Yang Luo",
"Arunprakash Jayaprakash",
"Gaojie Chen",
"Chong Huang",
"Qu Luo",
"Pei Xiao"
] |
[] | 2025-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-tractable-approach-to-massive-communication
|
2506.16304
| null | null |
A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization
|
The full-scale 6G standardization has attracted considerable recent attention, especially since the first 3GPP-wide 6G workshop held in March 2025. To understand the practical and fundamental values of 6G and facilitate its standardization, it is crucial to explore the theoretical limits of spectrum, energy, and coverage efficiency considering practical hardware and signaling constraints. In this paper, we present a mean-field-approximation-based investigation on two out of six use case scenarios defined by IMT-2030, namely, massive communication and ubiquitous connectivity. Being aware of the limitation in interference cancellation owing to constrained cost and hardware complexity, we investigate the spectrum reuse architecture in both usage scenarios. We propose a tractable spectrum reuse with low signaling overhead consumed for channel estimation and channel state information (CSI) feedback. Our analysis indicates that the massive communication over cellular and device-to-device (D2D) networks can benefit from channel orthogonalization, while it is unnecessary to share the CSI of interfering links. Moreover, deploying relays or movable base stations, e.g. unmanned aerial vehicle, yields substantial energy and spectrum gain for ubiquitous connectivity, despite introducing interference. As such, the mean-field-optimization-based evaluation is expected to positively impact 6G and NextG standardization in 3GPP and other standardization bodies.
| null |
https://arxiv.org/abs/2506.16304v1
|
https://arxiv.org/pdf/2506.16304v1.pdf
| null |
[
"Junyi Jiang",
"Wei Chen",
"Xin Guo",
"Shenghui Song",
"Ying Jun",
"Zhang",
"Zhu Han",
"Merouane Debbah",
"Khaled B. Letaief"
] |
[] | 2025-06-19T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "We propose to theoretically and empirically examine the effect of incorporating weighting schemes into walk-aggregating GNNs. To this end, we propose a simple, interpretable, and end-to-end supervised GNN model, called AWARE (Attentive Walk-Aggregating GRaph Neural NEtwork), for graph-level prediction. AWARE aggregates the walk information by means of weighting schemes at distinct levels (vertex-, walk-, and graph-level) in a principled manner. By virtue of the incorporated weighting schemes at these different levels, AWARE can emphasize the information important for prediction while diminishing the irrelevant ones—leading to representations that can improve learning performance.",
"full_name": "Attentive Walk-Aggregating Graph Neural Network",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "AWARE",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/refining-ray-tracing-accuracy-and-efficiency
|
2506.16236
| null | null |
Refining Ray-Tracing Accuracy and Efficiency in the Context of FRMCS Urban Railway Channel Predictions
|
The upcoming roll-out of the new wireless communication standard for wireless railway services, FRMCS, requires a thorough understanding of the system performance in real-world conditions, since this will strongly influence the deployment costs and the effectiveness of an infrastructure planned for decades. The virtual testing of the equipment and network performance in realistic simulated scenarios is key; its accuracy depends on the reliability of the predicted radio channel properties. In this article, the authors explain how they are evolving a ray-tracing (RT) tool to apply it to the specific case of simulating the radio link between the FRMCS fixed infrastructure and an antenna placed on the roof of a train moving in an urban environment. First, a dynamic version of the RT tool is used to capture the rapid variations of all channel metrics; a compromise is sought between computation time and accuracy. Besides, a hybridization of RT and physical optics (PO) allows the integration of objects near the track, such as catenary pylons, into the simulation. A case study shows that the scattering by metallic pylons brings a significant contribution.
| null |
https://arxiv.org/abs/2506.16236v1
|
https://arxiv.org/pdf/2506.16236v1.pdf
| null |
[
"Romain Charbonnier",
"Thierry Tenoux",
"Yoann Corre"
] |
[] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/masc-integrated-sensing-and-communications
|
2506.16198
| null | null |
MASC: Integrated Sensing and Communications for the Martian Internet of Space
|
Mars exploration missions increasingly demand reliable communication systems, yet harsh environmental conditions -- particularly frequent dust storms, extreme Doppler effects, and stringent resource constraints -- pose unprecedented challenges to conventional communication approaches. This paper presents the Martian Adaptive Sensing and Communication (MASC) system specifically designed for the Martian environment. MASC establishes a physically interpretable channel model and develops three key components: environment-aware hybrid precoding, adaptive parameter mapping, and robust communication precoding. Simulation results demonstrate that MASC maintains 45 percent sensing coverage under severe dust conditions compared to only 5 percent with conventional methods, provides up to 2.5 dB signal-to-interference-plus-noise ratio (SINR) improvement at 50 percent channel state information (CSI) uncertainty, and yields 80 percent higher capacity in moderate dust storms. Using an epsilon-constraint multi-objective optimization approach, we enable mission planners to select operational modes ranging from communication-priority (0.33 bps/Hz capacity, 28 percent sensing coverage) to sensing-priority (90 percent coverage with minimal capacity), offering a versatile framework that balances environmental awareness with hyper-reliable data transmission. This work provides a validated blueprint for integrated sensing and communication (ISAC) in non-terrestrial networks (NTN), a key enabler for achieving ubiquitous connectivity in the 6G era.
| null |
https://arxiv.org/abs/2506.16198v1
|
https://arxiv.org/pdf/2506.16198v1.pdf
| null |
[
"Haofan Dong",
"Ozgur B. Akan"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dcfnet-doppler-correction-filter-network-for
|
2506.16191
| null | null |
DCFNet: Doppler Correction Filter Network for Integrated Sensing and Communication in Multi-User MIMO-OFDM Systems
|
Integrated sensing and communication (ISAC) is a headline feature for the forthcoming IMT-2030 and 6G releases, yet a concrete solution that fits within the established orthogonal frequency division multiplexing (OFDM) family remains open. Specifically, Doppler-induced inter-carrier interference (ICI) destroys sub-carrier orthogonality of OFDM sensing signals, blurring range-velocity maps and severely degrading sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) OFDM systems, this paper proposes Doppler-Correction Filter Network (DCFNet), an AI-native ISAC model that delivers fine range-velocity resolution at minimal complexity without altering the legacy frame structure. A bank of DCFs first shifts dominant ICI energy away from critical Doppler bins; a compact deep learning network then suppresses the ICI. To further enhance the range and velocity resolutions, we propose DCFNet with local refinement (DCFNet-LR), which applies a generalized likelihood ratio test (GLRT) to refine target estimates of DCFNet to sub-cell accuracy. Simulation results show that DCFNet-LR runs $143\times$ faster than maximum likelihood search and achieves significantly superior performance, reducing the range RMSE by up to $2.7 \times 10^{-4}$ times and the velocity RMSE by $6.7 \times 10^{-4}$ times compared to conventional detection methods.
| null |
https://arxiv.org/abs/2506.16191v1
|
https://arxiv.org/pdf/2506.16191v1.pdf
| null |
[
"Hyeonho Noh",
"Hyeonsu Lyu",
"Moe Z. Win",
"Hyun Jong Yang"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multigroup-multicast-design-for-pinching
|
2506.16184
| null | null |
Multigroup Multicast Design for Pinching-Antenna Systems: Waveguide-Division or Waveguide-Multiplexing?
|
This article addresses the design of multigroup multicast communications in the pinching-antenna system (PASS). A PASS-enabled multigroup transmission framework is proposed to maximize multicast rates under a couple of transmission architectures: waveguide-division (WD) and waveguide-multiplexing (WM). 1) For WD, an element-wise sequential optimization strategy is proposed for pinching beamforming, i.e., optimizing the activated positions of pinching antennas along dielectric waveguides. Meanwhile, a log-sum-exp projected gradient descent algorithm is proposed for transmit power allocation across waveguides. 2) For WM, a majorization-minimization (MM)-based framework is proposed to tackle the problem's non-smoothness and non-convexity. On this basis, a low-complexity element-wise sequential optimization method is developed for pinching beamforming using the MM surrogate objective. Furthermore, the optimal transmit beamformer structure is derived from the MM surrogate objective using the Lagrange duality, with an efficient transmit beamforming algorithm proposed using projected adaptive gradient descent. Numerical results demonstrate that: i) both WD and WM architectures in PASS achieve significant multicast rate improvements over conventional MIMO techniques, especially for systems with large service areas; ii) WM is more robust than WD in dense deployments, while WD excels when user groups are spatially separated.
| null |
https://arxiv.org/abs/2506.16184v1
|
https://arxiv.org/pdf/2506.16184v1.pdf
| null |
[
"Shan Shan",
"Chongjun Ouyang",
"Yong Li",
"Yuanwei Liu"
] |
[] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/towards-ai-driven-rans-for-6g-and-beyond
|
2506.16070
| null | null |
Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons
|
It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.
| null |
https://arxiv.org/abs/2506.16070v1
|
https://arxiv.org/pdf/2506.16070v1.pdf
| null |
[
"Mathushaharan Rathakrishnan",
"Samiru Gayan",
"Rohit Singh",
"Amandeep Kaur",
"Hazer Inaltekin",
"Sampath Edirisinghe",
"H. Vincent Poor"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-scalable-factorization-approach-for-high
|
2506.16032
| null | null |
A Scalable Factorization Approach for High-Order Structured Tensor Recovery
|
Tensor decompositions, which represent an $N$-order tensor using approximately $N$ factors of much smaller dimensions, can significantly reduce the number of parameters. This is particularly beneficial for high-order tensors, as the number of entries in a tensor grows exponentially with the order. Consequently, they are widely used in signal recovery and data analysis across domains such as signal processing, machine learning, and quantum physics. A computationally and memory-efficient approach to these problems is to optimize directly over the factors using local search algorithms such as gradient descent, a strategy known as the factorization approach in matrix and tensor optimization. However, the resulting optimization problems are highly nonconvex due to the multiplicative interactions between factors, posing significant challenges for convergence analysis and recovery guarantees. In this paper, we present a unified framework for the factorization approach to solving various tensor decomposition problems. Specifically, by leveraging the canonical form of tensor decompositions--where most factors are constrained to be orthonormal to mitigate scaling ambiguity--we apply Riemannian gradient descent (RGD) to optimize these orthonormal factors on the Stiefel manifold. Under a mild condition on the loss function, we establish a Riemannian regularity condition for the factorized objective and prove that RGD converges to the ground-truth tensor at a linear rate when properly initialized. Notably, both the initialization requirement and the convergence rate scale polynomially rather than exponentially with $N$, improving upon existing results for Tucker and tensor-train format tensors.
| null |
https://arxiv.org/abs/2506.16032v1
|
https://arxiv.org/pdf/2506.16032v1.pdf
| null |
[
"Zhen Qin",
"Michael B. Wakin",
"Zhihui Zhu"
] |
[
"Tensor Decomposition"
] | 2025-06-19T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "TuckER",
"full_name": "TuckER",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "\n\ngraph embeddings, can be homogeneous graph or heterogeneous graph",
"name": "Graph Embeddings",
"parent": null
},
"name": "TuckER",
"source_title": "TuckER: Tensor Factorization for Knowledge Graph Completion",
"source_url": "https://arxiv.org/abs/1901.09590v2"
}
] |
https://paperswithcode.com/paper/multi-domain-optimization-framework-for-isac
|
2506.16011
| null | null |
Multi-Domain Optimization Framework for ISAC: From Electromagnetic Shaping to Network Cooperation
|
Integrated sensing and communication (ISAC) has emerged as a key feature for sixth-generation (6G) networks, providing an opportunity to meet the dual demands of communication and sensing. Existing ISAC research primarily focuses on baseband optimization at individual access points, with limited attention to the roles of electromagnetic (EM) shaping and network-wide coordination. The intricate interdependencies between these domains remain insufficiently explored, leaving their full potential for enhancing ISAC performance largely untapped. To bridge this gap, we consider multi-domain ISAC optimization integrating EM shaping, baseband processing, and network cooperation strategies that facilitate efficient resource management and system-level design. We analyze the fundamental trade-offs between these domains and offer insights into domain-specific and cross-domain strategies contributing to ISAC performance and efficiency. We then conduct a case study demonstrating the effectiveness of joint multi-domain optimization. Finally, we discuss key challenges and future research directions to connect theoretical advancements and practical ISAC deployments. This work paves the way for intelligent and scalable ISAC architectures, providing critical insights for their seamless integration into next-generation wireless networks.
| null |
https://arxiv.org/abs/2506.16011v1
|
https://arxiv.org/pdf/2506.16011v1.pdf
| null |
[
"Rang Liu",
"Ming Li",
"Mehdi Zafari",
"Bjorn Ottersten",
"A. Lee Swindlehurst"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/exploiting-both-pilots-and-data-payloads-for
|
2506.15998
| null | null |
Exploiting Both Pilots and Data Payloads for Integrated Sensing and Communications
|
Integrated sensing and communications (ISAC) is one of the key enabling technologies in future sixth-generation (6G) networks. Current ISAC systems predominantly rely on deterministic pilot signals within the signal frame to accomplish sensing tasks. However, these pilot signals typically occupy only a small portion, e.g., 0.15% to 25%, of the time-frequency resources. To enhance the system utility, a promising solution is to repurpose the extensive random data payload signals for sensing tasks. In this paper, we analyze the ISAC performance of a multi-antenna system where both deterministic pilot and random data symbols are employed for sensing tasks. By capitalizing on random matrix theory (RMT), we first derive a semi-closed-form asymptotic expression of the ergodic linear minimum mean square error (ELMMSE). Then, we formulate an ISAC precoding optimization problem to minimize the ELMMSE, which is solved via a specifically tailored successive convex approximation (SAC) algorithm. To provide system insights, we further derive a closed-form expression for the asymptotic ELMMSE at high signal-to-noise ratios (SNRs). Our analysis reveals that, compared with conventional sensing implemented by deterministic signals, the sensing performance degradation induced by random signals is critically determined by the ratio of the transmit antenna size to the data symbol length. Based on this result, the ISAC precoding optimization problem at high SNRs is transformed into a convex optimization problem that can be efficiently solved. Simulation results validate the accuracy of the derived asymptotic expressions of ELMMSE and the performance of the proposed precoding schemes. Particularly, by leveraging data payload signals for sensing tasks, the sensing error is reduced by up to 5.6 dB compared to conventional pilot-based sensing.
| null |
https://arxiv.org/abs/2506.15998v1
|
https://arxiv.org/pdf/2506.15998v1.pdf
| null |
[
"Chen Xu",
"Xianghao Yu",
"Fan Liu",
"Shi Jin"
] |
[
"ISAC"
] | 2025-06-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/theoretical-analysis-of-near-field-mimo
|
2506.15972
| null | null |
Theoretical Analysis of Near-Field MIMO Channel Capacity and Mid-Band Experimental Validation
|
With the increase of multiple-input-multiple-output (MIMO) array size and carrier frequency, near-field MIMO communications will become crucial in 6G wireless networks. Due to the increase of MIMO near-field range, the research of near-field MIMO capacity has aroused wide interest. In this paper, we focus on the theoretical analysis and empirical study of near-field MIMO capacity. First, the near-field channel model is characterized from the electromagnetic information perspective. Second, with the uniform planar array (UPA), the channel capacity based on effective degree of freedom (EDoF) is analyzed theoretically, and the closed-form analytical expressions are derived in detail. Finally, based on the numerical verification of near-field channel measurement experiment at 13 GHz band, we reveal that the channel capacity of UPA-type MIMO systems decreases continuously with the communication distance increasing. It can be observed that the near-field channel capacity gain is relatively obvious when large-scale MIMO is adopted at both receiving and transmitter ends, but the near-field channel capacity gain may be limited in the actual communication system with the small antenna array at receiving end. This work will give some reference to the near-field communication systems.
| null |
https://arxiv.org/abs/2506.15972v1
|
https://arxiv.org/pdf/2506.15972v1.pdf
| null |
[
"Haiyang Miao",
"Jianhua Zhang",
"Pan Tang",
"Heng Wang",
"Lei Tian",
"Guangyi Liu"
] |
[] | 2025-06-19T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/fractional-reasoning-via-latent-steering
|
2506.15882
| null | null |
Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute
|
Test-time compute has emerged as a powerful paradigm for improving the performance of large language models (LLMs), where generating multiple outputs or refining individual chains can significantly boost answer accuracy. However, existing methods like Best-of-N, majority voting, and self-reflection typically apply reasoning in a uniform way across inputs, overlooking the fact that different problems may require different levels of reasoning depth. In this work, we propose Fractional Reasoning, a training-free and model-agnostic framework that enables continuous control over reasoning intensity at inference time, going beyond the limitations of fixed instructional prompts. Our method operates by extracting the latent steering vector associated with deeper reasoning and reapplying it with a tunable scaling factor, allowing the model to tailor its reasoning process to the complexity of each input. This supports two key modes of test-time scaling: (1) improving output quality in breadth-based strategies (e.g., Best-of-N, majority voting), and (2) enhancing the correctness of individual reasoning chains in depth-based strategies (e.g., self-reflection). Experiments on GSM8K, MATH500, and GPQA demonstrate that Fractional Reasoning consistently improves performance across diverse reasoning tasks and models.
| null |
https://arxiv.org/abs/2506.15882v1
|
https://arxiv.org/pdf/2506.15882v1.pdf
| null |
[
"Sheng Liu",
"Tianlang Chen",
"Pan Lu",
"Haotian Ye",
"Yizheng Chen",
"Lei Xing",
"James Zou"
] |
[
"continuous-control",
"Continuous Control",
"GSM8K"
] | 2025-06-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/near-field-swipt-with-gmimo-in-the-upper-mid
|
2506.15670
| null | null |
Near-Field SWIPT with gMIMO in the Upper Mid-Band: Opportunities, Challenges, and the Way Forward
|
This paper explores the integration of simultaneous wireless information and power transfer (SWIPT) with gigantic multiple-input multiple-output (gMIMO) technology operating in the upper mid-band frequency range (7-24 GHz). The near-field propagation achieved by gMIMO introduces unique opportunities for energy-efficient, high-capacity communication systems that cater to the demands of 6G wireless networks. Exploiting spherical wave propagation, near-field SWIPT with gMIMO enables precise energy and data delivery, enhancing spectral efficiency through beamfocusing and massive spatial multiplexing. This paper discusses theoretical principles, design challenges, and enabling solutions, including advanced channel estimation techniques, precoding strategies, and dynamic array configurations such as sparse and modular arrays. Through analytical insights and a case study, this paper demonstrates the feasibility of achieving optimized energy harvesting and data throughput in dense and dynamic environments. These findings contribute to advancing energy-autonomous Internet-of-Everything (IoE) deployments, smart factory networks, and other energy-autonomous applications aligned with the goals of next-generation wireless technologies.
| null |
https://arxiv.org/abs/2506.15670v1
|
https://arxiv.org/pdf/2506.15670v1.pdf
| null |
[
"Özlem Tugfe Demir",
"Mustafa Ozger",
"Ferdi Kara",
"Woong-Hee Lee",
"Emil Björnson"
] |
[] | 2025-06-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/analyzing-ura-geometry-for-enhanced-spatial
|
2506.15470
| null | null |
Analyzing URA Geometry for Enhanced Spatial Multiplexing and Extended Near-Field Coverage
|
With the deployment of large antenna arrays at high frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused within a spatial region of finite depth, enabling spatial multiplexing in both the angular and range dimensions. This paper derives the beamdepth for a generalized uniform rectangular array (URA) and investigates how array geometry influences the near-field beamdepth and the limits where near-field beamfocusing is achievable. To characterize the near-field boundary in terms of beamfocusing and spatial multiplexing gains, we define the effective beamfocusing Rayleigh distance (EBRD) for a generalized URA. Our analysis reveals that while a square URA achieves the narrowest beamdepth, the EBRD is maximized for a wide or tall URA. However, despite its narrow beamdepth, a square URA may experience a reduction in multiuser sum rate due to its severely constrained EBRD. Simulation results confirm that a wide or tall URA achieves a sum rate of 3.5 X more than that of a square URA, benefiting from the extended EBRD and improved spatial multiplexing capabilities.
| null |
https://arxiv.org/abs/2506.15470v1
|
https://arxiv.org/pdf/2506.15470v1.pdf
| null |
[
"Ahmed Hussain",
"Asmaa Abdallah",
"Abdulkadir Celik",
"Ahmed M. Eltawil"
] |
[] | 2025-06-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/effect-of-signal-quantization-on-performance
|
2506.15463
| null | null |
Effect of Signal Quantization on Performance Measures of a 1st Order One Dimensional Differential Microphone Array
|
In practical systems, recorded analog signals must be digitized for processing, introducing quantization as a critical aspect of data acquisition. While prior studies have examined quantization effects in various signal processing contexts, its impact on differential microphone arrays (DMAs), particularly in one-dimensional (1D) first-order configurations, remains unexplored. This paper investigates the influence of signal quantization on performance of first-order 1D DMAs across various beampatterns. An analytical expression for quantized beamformed output for a first-order 1D DMA has been formulated. The effect of signal quantization has been studied on array performance measures such as the Beampattern, Directivity Factor (DF), Front-to-Back Ratio (FBR), and null depth (ND). Simulation results reveal that beampattern shape remains structurally invariant across quantization bit depths, with quantization primarily affecting ND. DF and FBR remain constant with the varying number of quantization bits. Additionally, ND is shown to be frequency-independent; however, it increases with increasing quantization bit depths, enhancing interference suppression. The study also examines the effect of steering nulls across the azimuthal range, showing that ND degrades as the null moves closer to the source look direction, indicating reduced interference suppression.
| null |
https://arxiv.org/abs/2506.15463v1
|
https://arxiv.org/pdf/2506.15463v1.pdf
| null |
[
"Shweta Pal",
"Arun Kumar",
"Monika Agrawal"
] |
[
"Quantization"
] | 2025-06-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In image inpainting task, the mechanism extracts complementary features from the word embedding in two paths by reciprocal attention, which is done by comparing the descriptive text and complementary image areas through reciprocal attention.",
"full_name": "Dual Multimodal Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "DMA",
"source_title": "Text-Guided Neural Image Inpainting",
"source_url": "https://arxiv.org/abs/2004.03212v4"
}
] |
https://paperswithcode.com/paper/urban-ris-assisted-hap-networks-performance
|
2506.15338
| null | null |
Urban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
|
This paper studies a high-altitude platform (HAP) network supported by reconfigurable intelligent surfaces (RISs). The practical irregular placement of HAPs and RISs is modeled using homogeneous Poisson point processes, while buildings that cause blockages in urban areas are modeled as a Boolean scheme of rectangles. We introduce a novel approach to characterize the statistical channel based on generalized Beta prime distribution. Analytical expressions for coverage probability and ergodic capacity in an interference-limited system are derived and validated through Monte Carlo simulations. The findings show notable performance improvements and reveal the impact of various system parameters, including blockages effect which contribute in mitigating interference from the other visible HAPs. This proposed system could enhance connectivity and enable effective data offloading in urban environments.
| null |
https://arxiv.org/abs/2506.15338v1
|
https://arxiv.org/pdf/2506.15338v1.pdf
| null |
[
"Islam M. Tanash",
"Ayush Kumar Dwivedi",
"Taneli Riihonen"
] |
[
"Point Processes"
] | 2025-06-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-smooshed-bmocz-zero-constellation-for-cfo
|
2506.12599
| null | null |
A Smooshed BMOCZ Zero Constellation for CFO Estimation Without Channel Coding
|
In this study, we propose a new binary modulation on conjugate-reciprocal zeros (BMOCZ) zero constellation, which we call smooshed binary modulation on conjugate-reciprocal zeros (SBMOCZ), to address carrier frequency offset (CFO)-induced zero rotation without depending on channel coding. In our approach, we modify the phase mapping of Huffman BMOCZ by shrinking the angle between adjacent zeros, except for the first and last, to introduce a gap in the zero constellation. By discerning the gap location in the received polynomial, the receiver can estimate and correct the phase rotation. We demonstrate the error rate performance of SBMOCZ relative to Huffman BMOCZ, showing that SBMOCZ addresses a CFO-induced rotation at the cost of a modest performance reduction compared to Huffman BMOCZ in the absence of a CFO. Finally, we compare SBMOCZ to Huffman BMOCZ using a cyclically permutable code (CPC), showing a 4 dB bit error rate (BER) improvement in a fading channel, while demonstrating comparable performance across other simulations.
| null |
https://arxiv.org/abs/2506.12599v2
|
https://arxiv.org/pdf/2506.12599v2.pdf
| null |
[
"Anthony Joseph Perre",
"Parker Huggins",
"Alphan Sahin"
] |
[] | 2025-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reinforcement-learning-based-policy
|
2506.15273
| null | null |
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
|
Flexible and efficient wireless resource sharing across heterogeneous services is a key objective for future wireless networks. In this context, we investigate the performance of a system where latency-constrained internet-of-things (IoT) devices coexist with a broadband user. The base station adopts a grant-free access framework to manage resource allocation, either through orthogonal radio access network (RAN) slicing or by allowing shared access between services. For the IoT users, we propose a reinforcement learning (RL) approach based on double Q-Learning (QL) to optimise their repetition-based transmission strategy, allowing them to adapt to varying levels of interference and meet a predefined latency target. We evaluate the system's performance in terms of the cumulative distribution function of IoT users' latency, as well as the broadband user's throughput and energy efficiency (EE). Our results show that the proposed RL-based access policies significantly enhance the latency performance of IoT users in both RAN Slicing and RAN Sharing scenarios, while preserving desirable broadband throughput and EE. Furthermore, the proposed policies enable RAN Sharing to be energy-efficient at low IoT traffic levels, and RAN Slicing to be favourable under high IoT traffic.
| null |
https://arxiv.org/abs/2506.15273v1
|
https://arxiv.org/pdf/2506.15273v1.pdf
| null |
[
"Anup Mishra",
"Čedomir Stefanović",
"Xiuqiang Xu",
"Petar Popovski",
"Israel Leyva-Mayorga"
] |
[
"Q-Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2025-06-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
},
{
"code_snippet_url": "",
"description": "**Double Q-learning** is an off-policy reinforcement learning algorithm that utilises double estimation to counteract overestimation problems with traditional Q-learning. \r\n\r\nThe max operator in standard [Q-learning](https://paperswithcode.com/method/q-learning) and [DQN](https://paperswithcode.com/method/dqn) uses the same values both to select and to evaluate an action. This makes it more likely to select overestimated values, resulting in overoptimistic value estimates. To prevent this, we can decouple the selection from the evaluation, which is the idea behind Double Q-learning:\r\n\r\n$$ Y^{Q}\\_{t} = R\\_{t+1} + \\gamma{Q}\\left(S\\_{t+1}, \\arg\\max\\_{a}Q\\left(S\\_{t+1}, a; \\mathbb{\\theta}\\_{t}\\right);\\mathbb{\\theta}\\_{t}\\right) $$\r\n\r\nThe Double Q-learning error can then be written as:\r\n\r\n$$ Y^{DoubleQ}\\_{t} = R\\_{t+1} + \\gamma{Q}\\left(S\\_{t+1}, \\arg\\max\\_{a}Q\\left(S\\_{t+1}, a; \\mathbb{\\theta}\\_{t}\\right);\\mathbb{\\theta}^{'}\\_{t}\\right) $$\r\n\r\nHere the selection of the action in the $\\arg\\max$ is still due to the online weights $\\theta\\_{t}$. But we use a second set of weights $\\mathbb{\\theta}^{'}\\_{t}$ to fairly evaluate the value of this policy.\r\n\r\nSource: [Deep Reinforcement Learning with Double Q-learning](https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q)",
"full_name": "Double Q-learning",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Double Q-learning",
"source_title": "Double Q-learning",
"source_url": "http://papers.nips.cc/paper/3964-double-q-learning"
},
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right)\\right] $$\r\n\r\nThe learned action-value function $Q$ directly approximates $q\\_{*}$, the optimal action-value function, independent of the policy being followed.\r\n\r\nSource: Sutton and Barto, Reinforcement Learning, 2nd Edition",
"full_name": "Q-Learning",
"introduced_year": 1984,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Q-Learning",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/enhancing-eloran-timing-accuracy-via-machine
|
2506.15235
| null | null |
Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data
|
The vulnerabilities of global navigation satellite systems (GNSS) to signal interference have increased the demand for complementary positioning, navigation, and timing (PNT) systems. To address this, South Korea has decided to deploy an enhanced long-range navigation (eLoran) system as a complementary PNT solution. Similar to GNSS, eLoran provides highly accurate timing information, which is essential for applications such as telecommunications, financial systems, and power distribution. However, the primary sources of error for GNSS and eLoran differ. For eLoran, the main source of error is signal propagation delay over land, known as the additional secondary factor (ASF). This delay, influenced by ground conductivity and weather conditions along the signal path, is challenging to predict and mitigate. In this paper, we measure the time difference (TD) between GPS and eLoran using a time interval counter and analyze the correlations between eLoran/GPS TD and eleven meteorological factors. Accurate estimation of eLoran/GPS TD could enable eLoran to achieve timing accuracy comparable to that of GPS. We propose two estimation models for eLoran/GPS TD and compare their performance with existing TD estimation methods. The proposed WLR-AGRNN model captures the linear relationships between meteorological factors and eLoran/GPS TD using weighted linear regression (WLR) and models nonlinear relationships between outputs from expert networks through an anisotropic general regression neural network (AGRNN). The model incorporates terrain elevation to appropriately weight meteorological data, as elevation influences signal propagation delay. Experimental results based on four months of data demonstrate that the WLR-AGRNN model outperforms other models, highlighting its effectiveness in improving eLoran/GPS TD estimation accuracy.
| null |
https://arxiv.org/abs/2506.15235v1
|
https://arxiv.org/pdf/2506.15235v1.pdf
| null |
[
"Taewon Kang",
"Seunghyeon Park",
"Pyo-Woong Son",
"Jiwon Seo"
] |
[] | 2025-06-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predicted values $\\hat{y} = \\textbf{X}\\hat{\\beta}$ and actual values $y$: $\\left(y-\\textbf{X}\\beta\\right)^{2}$.\r\n\r\nWe can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\\hat{\\beta}$.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)",
"full_name": "Linear Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Linear Regression",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Greedy Policy Search** (GPS) is a simple algorithm that learns a policy for test-time data augmentation based on the predictive performance on a validation set. GPS starts with an empty policy and builds it in an iterative fashion. Each step selects a sub-policy that provides the largest improvement in calibrated log-likelihood of ensemble predictions and adds it to the current policy.",
"full_name": "Greedy Policy Search",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Data Augmentation** refers to a class of methods that augment an image dataset to increase the effective size of the training set, or as a form of regularization to help the network learn more effective representations.",
"name": "Image Data Augmentation",
"parent": null
},
"name": "GPS",
"source_title": "Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation",
"source_url": "https://arxiv.org/abs/2002.09103v2"
}
] |
https://paperswithcode.com/paper/joint-computation-offloading-and-resource-1
|
2506.15225
| null | null |
Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels
|
The computation demands from the maritime Internet of Things (MIoT) increase rapidly in recent years, and the unmanned aerial vehicles (UAVs) and vessels based multi-access edge computing (MEC) can fulfill these MIoT requirements. However, the uncertain maritime tasks present significant challenges of inefficient computation offloading and resource allocation. In this paper, we focus on the maritime computation offloading and resource allocation through the cooperation of UAVs and vessels, with consideration of uncertain tasks. Specifically, we propose a cooperative MEC framework for computation offloading and resource allocation, including MIoT devices, UAVs and vessels. Then, we formulate the optimization problem to minimize the total execution time. As for the uncertain MIoT tasks, we leverage Lyapunov optimization to tackle the unpredictable task arrivals and varying computational resource availability. By converting the long-term constraints into short-term constraints, we obtain a set of small-scale optimization problems. Further, considering the heterogeneity of actions and resources of UAVs and vessels, we reformulate the small-scale optimization problem into a Markov game (MG). Moreover, a heterogeneous-agent soft actor-critic is proposed to sequentially update various neural networks and effectively solve the MG problem. Finally, simulations are conducted to verify the effectiveness in addressing computational offloading and resource allocation.
| null |
https://arxiv.org/abs/2506.15225v1
|
https://arxiv.org/pdf/2506.15225v1.pdf
| null |
[
"Jiahao You",
"Ziye Jia",
"Chao Dong",
"Qihui Wu",
"Zhu Han"
] |
[
"Edge-computing"
] | 2025-06-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/out-of-band-modality-synergy-based-multi-user
|
2506.15136
| null | null |
Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead
|
Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.
| null |
https://arxiv.org/abs/2506.15136v1
|
https://arxiv.org/pdf/2506.15136v1.pdf
| null |
[
"Kehui Li",
"Binggui Zhou",
"Jiajia Guo",
"Feifei Gao",
"Guanghua Yang",
"Shaodan Ma"
] |
[
"Beam Prediction",
"Management"
] | 2025-06-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/fiber-signal-denoising-algorithm-using-hybrid
|
2506.15125
| null | null |
Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
|
With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-term memory (LSTM) for sequential processing. Additionally, a line-by-line matching algorithm for vehicle detection and tracking is introduced, thus realizing the complete processing of fiber signal denoising and feature extraction. Experiments were carried out on a self-established real highway tunnel dataset, showing that our proposed hybrid network yields more satisfactory denoising performance than Spatial-domain DAE.
| null |
https://arxiv.org/abs/2506.15125v1
|
https://arxiv.org/pdf/2506.15125v1.pdf
| null |
[
"LinLin Wang",
"Wei Wang",
"Dezhao Wang",
"Shanwen Wang"
] |
[
"Deep Learning",
"Denoising",
"vehicle detection"
] | 2025-06-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/secure-time-modulated-intelligent-reflecting
|
2506.14992
| null | null |
Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
|
We propose a novel directional modulation (DM) design for OFDM transmitters aided by a time-modulated intelligent reflecting surface (TM-IRS). The TM-IRS is configured to preserve the integrity of transmitted signals toward multiple legitimate users while scrambling the signal in all other directions. Existing TM-IRS design methods typically target a single user direction and follow predefined rule-based procedures, making them unsuitable for multi-user scenarios. Here, we propose a generative AI-based approach to design good sets of TM-IRS parameters out of a set of all possible quantized ranges of parameters. The design objective is to maximize the sum rate across the authorized directions. We model the TM-IRS parameter selection as a deterministic Markov decision process (MDP), where each terminal state corresponds to a specific configuration of TM-IRS parameters. GFlowNets are employed to learn a stochastic policy that samples TM-IRS parameter sets with probability proportional to their associated sum rate reward. Experimental results demonstrate that the proposed method effectively enhances the security of the TM-IRS-aided OFDM systems with multi-users. Also, despite the vast size of the TM-IRS configuration space, the GFlowNet is able to converge after training on fewer than 0.000001% of all possible configurations, demonstrating remarkable efficiency compared to exhaustive combinatorial search. Implementation code is available at https://github.com/ZhihaoTao/GFN4TM-RIS to facilitate reproducibility.
|
The design objective is to maximize the sum rate across the authorized directions.
|
https://arxiv.org/abs/2506.14992v1
|
https://arxiv.org/pdf/2506.14992v1.pdf
| null |
[
"Zhihao Tao",
"Athina P. Petropulu"
] |
[] | 2025-06-17T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/metasurfaces-integrated-doubly-dispersive
|
2506.14985
| null | null |
Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization
|
The doubly-dispersive (DD) channel structure has played a pivotal role in wireless communications, particularly in high-mobility scenarios and integrated sensing and communications (ISAC), due to its ability to capture the key fading effects experienced by a transmitted signal as it propagates through a dynamic medium. However, extending the DD framework to multiple-input multiple-output (MIMO) systems, especially in environments artificially enhanced by reconfigurable intelligent surfaces (RISs) and stacked intelligent metasurfaces (SIM), remains a challenging open problem. In this chapter, a novel metasurfaces-parametrized DD (MPDD) channel model that integrates an arbitrary number of RISs, while also incorporating SIM at both the transmitter and receiver is introduced. Next, the application of this model to some key waveforms optimized for DD environments -- namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) -- is discussed. Finally, the programmability of the proposed model is highlighted through an illustrative application, demonstrating its potential for enhancing waveform performance in SIM-assisted wireless systems.
| null |
https://arxiv.org/abs/2506.14985v1
|
https://arxiv.org/pdf/2506.14985v1.pdf
| null |
[
"Kuranage Roche Rayan Ranasinghe",
"Hyeon Seok Rou",
"Iván Alexander Morales Sandoval",
"Giuseppe Thadeu Freitas de Abreu",
"George C. Alexandropoulos"
] |
[
"ISAC"
] | 2025-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/integrating-movable-antennas-and-intelligent-1
|
2506.14636
| null | null |
Integrating Movable Antennas and Intelligent Reflecting Surfaces (MA-IRS): Fundamentals, Practical Solutions, and Opportunities
|
Movable antennas (MAs) and intelligent reflecting surfaces (IRSs) enable active antenna repositioning and passive phase-shift tuning for channel reconfiguration, respectively. Integrating MAs and IRSs boosts spatial degrees of freedom, significantly enhancing wireless network capacity, coverage, and reliability. In this article, we first present the fundamentals of MA-IRS integration, involving clarifying the key design issues, revealing performance gain, and identifying the conditions where MA-IRS synergy persists. Then, we examine practical challenges and propose pragmatic design solutions, including optimization schemes, hardware architectures, deployment strategies, and robust designs for hardware impairments and mobility management. In addition, we highlight how MA-IRS architectures uniquely support advanced integrated sensing and communication, enhancing sensing performance and dual-functional flexibility. Overall, MA-IRS integration emerges as a compelling approach toward next-generation reconfigurable wireless systems.
| null |
https://arxiv.org/abs/2506.14636v1
|
https://arxiv.org/pdf/2506.14636v1.pdf
| null |
[
"Qingqing Wu",
"Ziyuan Zheng",
"Ying Gao",
"Weidong Mei",
"Xin Wei",
"Wen Chen",
"Boyu Ning"
] |
[
"Integrated sensing and communication",
"Management"
] | 2025-06-17T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "This optimizer mix [ADAM](https://paperswithcode.com/method/adam) and [SGD](https://paperswithcode.com/method/sgd) creating the MAS optimizer.",
"full_name": "Mixing Adam and SGD",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "MAS",
"source_title": "Mixing ADAM and SGD: a Combined Optimization Method",
"source_url": "https://arxiv.org/abs/2011.08042v1"
}
] |
https://paperswithcode.com/paper/performance-characterization-of-continuous
|
2506.14385
| null | null |
Performance Characterization of Continuous Reconfigurable Intelligent Surfaces
|
We consider a reconfigurable intelligent surface (RIS) that can implement a phase rotation continuously over the whole surface rather than via a finite number of discrete elements. Such an RIS can be considered a design for future systems where advances in metamaterials make such an implementation feasible or as the limiting case where the number of elements in a traditional RIS increases in a given area. We derive the optimal RIS design for the single-user (SU) scenario assuming a line-of-sight (LoS) from the RIS to the base station (BS) and correlated Rayleigh fading for the other links. We also derive the associated optimal signal-to-noise ratio (SNR) and its mean, a bound on the mean spectral efficiency (SE), an approximation to the SNR outage probability and an approximation to the coefficient of variation for the investigation of channel hardening.
| null |
https://arxiv.org/abs/2506.14385v1
|
https://arxiv.org/pdf/2506.14385v1.pdf
| null |
[
"Amy S. Inwood",
"Peter J. Smith",
"Mahmoud AlaaEldin",
"Michail Matthaiou"
] |
[] | 2025-06-17T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/lightweight-node-selection-in-hexagonal-grid
|
2506.14311
| null | null |
Lightweight Node Selection in Hexagonal Grid Topology for TDoA-Based UAV Localization
|
This paper investigates the optimization problem for TDoA-based UAV localization in low-altitude urban environments with hexagonal grid node deployment. We derive a lightweight optimized node selection strategy based on only RSSI measurements, to pre-select optimal nodes, avoiding extensive TDoA measurements in energy-constrained UAV scenarios. Theoretical and simulation results demonstrate that dynamically selecting the number of reference nodes improves localization performance while minimizing resource overhead.
| null |
https://arxiv.org/abs/2506.14311v1
|
https://arxiv.org/pdf/2506.14311v1.pdf
| null |
[
"Zexin Fang",
"Bin Han",
"Wenwen Chen",
"Hans D. Schotten"
] |
[] | 2025-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/distributed-activity-detection-for-cell-free
|
2506.14254
| null | null |
Distributed Activity Detection for Cell-Free Hybrid Near-Far Field Communications
|
A great amount of endeavor has recently been devoted to activity detection for massive machine-type communications in cell-free massive MIMO. However, in practice, as the number of antennas at the access points (APs) increases, the Rayleigh distance that separates the near-field and far-field regions also expands, rendering the conventional assumption of far-field propagation alone impractical. To address this challenge, this paper considers a hybrid near-far field activity detection in cell-free massive MIMO, and establishes a covariance-based formulation, which facilitates the development of a distributed algorithm to alleviate the computational burden at the central processing unit (CPU). Specifically, each AP performs local activity detection for the devices and then transmits the detection result to the CPU for further processing. In particular, a novel coordinate descent algorithm based on the Sherman-Morrison-Woodbury update with Taylor expansion is proposed to handle the local detection problem at each AP. Moreover, we theoretically analyze how the hybrid near-far field channels affect the detection performance. Simulation results validate the theoretical analysis and demonstrate the superior performance of the proposed approach compared with existing approaches.
| null |
https://arxiv.org/abs/2506.14254v1
|
https://arxiv.org/pdf/2506.14254v1.pdf
| null |
[
"Jingreng Lei",
"Yang Li",
"Zeyi Ren",
"Qingfeng Lin",
"Ziyue Wang",
"Ya-Feng Liu",
"Yik-Chung Wu"
] |
[
"Action Detection",
"Activity Detection",
"CPU"
] | 2025-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-comprehensive-survey-on-underwater-acoustic
|
2506.14165
| null | null |
A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
|
Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking has become a prominent research area of underwater communications and networking. Existing literature reviews often offer a narrow perspective or inadequately address the paradigm shifts driven by emerging technologies like deep learning and reinforcement learning. To address these gaps, this work presents a systematic survey of this field and introduces an innovative multidimensional taxonomy framework based on target scale, sensor perception modes, and sensor collaboration patterns. Within this framework, we comprehensively survey the literature (more than 180 publications) over the period 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as federated learning, blockchain, embodied intelligence, and large models.
| null |
https://arxiv.org/abs/2506.14165v1
|
https://arxiv.org/pdf/2506.14165v1.pdf
| null |
[
"Zhong Yang",
"Zhengqiu Zhu",
"Yong Zhao",
"Yonglin Tian",
"Changjun Fan",
"Runkang Guo",
"Wenhao Lu",
"Jingwei Ge",
"Bin Chen",
"Yin Zhang",
"Guohua Wu",
"Rui Wang",
"Gyorgy Eigner",
"Guangquan Cheng",
"Jincai Huang",
"Zhong Liu",
"Jun Zhang",
"Imre J. Rudas",
"Fei-Yue Wang"
] |
[
"Federated Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Survey"
] | 2025-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dynamically-tunable-helical-antenna
|
2506.14065
| null | null |
Dynamically Tunable Helical Antenna
|
Unmanned aerial FPV systems demand ultra-low latency, high-reliability communication links. At high speeds and in cluttered environments, Doppler shifts and rapid multipath changes can dramatically raise packet error rates. This paper investigates these phenomena in the context of ExpressLRS (ELRS) long-range FPV control links and demonstrates a novel solution: real-time geometry tuning of a circularly polarized helical antenna array. This study integrates Maxwell-equation-based full-wave simulations (via Ansys HFSS) with controlled, blind field trials to validate performance. A new analysis framework incorporates Doppler-induced frequency offset into the antenna's radiation pattern and the system's error model. Compared to a conventional fixed antenna, the adaptive helical array shows a 20-30% PER reduction when drones exceed 150 mph. The adaptive system automatically adjusts coil pitch and diameter to retune the antenna as flight parameters (velocity, attitude) change. Measured VSWR stays near unity, preventing transmitter reflection spikes. RSSI variation is reduced by half, indicating stronger link stability in urban multi-path. A regression analysis confirms that the reduction in PER due to tuning is highly statistically significant. Calibration data and error analyses are provided to validate our methodology. These findings advance the understanding of high-mobility UAV communication channels and demonstrate that reconfigurable hardware-here, mechanically tunable helices-can effectively counter Doppler and multi-path impairments. The findings inform new design principles for UAV antenna arrays and suggest a path toward AI-integrated adaptive RF systems for drone swarms and racing platforms.
| null |
https://arxiv.org/abs/2506.14065v1
|
https://arxiv.org/pdf/2506.14065v1.pdf
| null |
[
"Ethan Chien",
"Jan Steckel"
] |
[
"Unity"
] | 2025-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pinching-antenna-systems-pass-meet-multiple
|
2506.13490
| null | null |
Pinching-Antenna Systems (PASS) Meet Multiple Access: NOMA or OMA?
|
A fundamental two-user PASS-based communication system is considered under three MA schemes, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). For each MA scheme, a pinching beamforming optimization problem is formulated to minimize the required transmit power for satisfying users' rate requirements. For NOMA and FDMA, a two-stage algorithm is proposed, where the locations of PAs are derived sequentially by using the successive convex approximation (SCA) method and fine-turning phase adjustment. For TDMA, by leveraging the time-switching feature of PASS, the optimal pinching beamforming of each time slot is derived to maximize the served user channel gain. Numerical results are provided to show that: 1) PASS can achieve a significant performance gain over conventional antenna systems, and 2) NOMA consistently outperforms FDMA, while TDMA provides superior performance than NOMA for symmetric user rate requirements.
| null |
https://arxiv.org/abs/2506.13490v1
|
https://arxiv.org/pdf/2506.13490v1.pdf
| null |
[
"Qiao Ren",
"Xidong Mu",
"Siyu Lin",
"Yuanwei Liu"
] |
[] | 2025-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/performance-analysis-of-communication-signals
|
2506.13330
| null | null |
Performance Analysis of Communication Signals for Localization in Underwater Sensor Networks
|
Fusion of passive and active measurements from sensor nodes becomes critical in localizing underwater objects and is traditionally achieved by communicating information to a central node. This causes significant inefficiencies in bandwidth, energy, and processing time, which are critical in marine applications. With integrated sensing and communication (ISAC) systems, the process of sensing, localization, and communication can be achieved jointly, and the inefficiencies can be minimized. Thus, the primary objective of this study is to analyse the efficacy of such communication signals in localizing a moving target in given underwater conditions. The Cram\'er-Rao Lower Bound (CRLB) is a performance metric used to determine the theoretical lower bound on localization errors. Simulation results illustrate the contours of localization error across various scenarios, offering valuable insights into system performance under different target dynamics and sea state conditions, showcasing their potential for efficient and reliable underwater localization applications.
| null |
https://arxiv.org/abs/2506.13330v1
|
https://arxiv.org/pdf/2506.13330v1.pdf
| null |
[
"Ashwani Koul",
"Gustaf Hendeby",
"Isaac Skog"
] |
[
"Integrated sensing and communication",
"ISAC"
] | 2025-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lightweight-task-oriented-semantic
|
2506.13243
| null | null |
Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models
|
Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements.
| null |
https://arxiv.org/abs/2506.13243v1
|
https://arxiv.org/pdf/2506.13243v1.pdf
| null |
[
"Chuanhong Liu",
"Caili Guo",
"Yang Yang",
"Mingzhe Chen",
"Tony Q. S. Quek"
] |
[
"Knowledge Distillation",
"Semantic Communication"
] | 2025-06-16T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://research.google/blog/auto-generated-summaries-in-google-docs/",
"description": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.\r\nSource: [Distilling the Knowledge in a Neural Network](https://arxiv.org/abs/1503.02531)",
"full_name": "Knowledge Distillation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Knowledge Distillation",
"parent": null
},
"name": "Knowledge Distillation",
"source_title": "Distilling the Knowledge in a Neural Network",
"source_url": "http://arxiv.org/abs/1503.02531v1"
}
] |
https://paperswithcode.com/paper/collaborative-beamforming-for-communication
|
2506.13014
| null | null |
Collaborative Beamforming for Communication Applications Using a Two-Element Fully-Wireless Open-Loop Coherent Distributed Array
|
In this work we demonstrate a proof of concept of a fully-wireless two-node open-loop coherent distributed communication system and evaluate its performance by transmitting QPSK , 64-, and 256-QAM constellations at a symbol rate of 2 MBd over a 58 m link in an urban environment. The system is implemented in a distributed manner with on-node processing using software-defined radios (SDRs) and wireless internode communication to share coordination information and does not rely on external time or frequency references such as the global navigation satellite system (GNSS). In each experiment ~100 messages were transmitted and a mean coherent gain of 0.936 was achieved across all measurements with a mean symbol error ratio of below $1.4\times 10^{-4}$ achieved up to 64-QAM, demonstrating a reliable bandwidth of up to 12 Mbps.
| null |
https://arxiv.org/abs/2506.13014v1
|
https://arxiv.org/pdf/2506.13014v1.pdf
| null |
[
"Jason M. Merlo",
"Jeffrey A. Nanzer"
] |
[] | 2025-06-16T00:00:00 | null | null | null | null |
[] |
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