Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Regression Codes for Multi-terminal Source and Channel Coding   We study a new class of codes for Gaussian multi-terminal source and channel
coding. These codes are designed using the statistical framework of
high-dimensional linear regression and are called Sparse Superposition or
Sparse Regression codes. Codewords are linear combinations of subsets of
columns of a design matrix. These codes were recently introduced by Barron and
Joseph and shown to achieve the channel capacity of AWGN channels with
computationally feasible decoding. They have also recently been shown to
achieve the optimal rate-distortion function for Gaussian sources. In this
paper, we demonstrate how to implement random binning and superposition coding
using sparse regression codes. In particular, with minimum-distance
encoding/decoding it is shown that sparse regression codes attain the optimal
information-theoretic limits for a variety of multi-terminal source and channel
coding problems.

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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph