Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Model and Data Driven Receiver Design for Data-Dependent
  Superimposed Training Scheme with Imperfect Hardware   Data-dependent superimposed training (DDST) scheme has shown the potential to
achieve high bandwidth efficiency, while encounters symbol misidentification
caused by hardware imperfection. To tackle these challenges, a joint model and
data driven receiver scheme is proposed in this paper. Specifically, based on
the conventional linear receiver model, the least squares (LS) estimation and
zero forcing (ZF) equalization are first employed to extract the initial
features for channel estimation and data detection. Then, shallow neural
networks, named CE-Net and SD-Net, are developed to refine the channel
estimation and data detection, where the imperfect hardware is modeled as a
nonlinear function and data is utilized to train these neural networks to
approximate it. Simulation results show that compared with the conventional
minimum mean square error (MMSE) equalization scheme, the proposed one
effectively suppresses the symbol misidentification and achieves similar or
better bit error rate (BER) performance without the second-order statistics
about the channel and noise.

31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics