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We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature…

Machine Learning · Statistics 2012-10-04 Krishnakumar Balasubramanian , Kai Yu , Guy Lebanon

Existing fixed-length feedback communication schemes are either specialized to particular channels (Schalkwijk--Kailath, Horstein), or apply to general channels but either have high coding complexity (block feedback schemes) or are…

Information Theory · Computer Science 2016-09-08 Cheuk Ting Li , Abbas El Gamal

Sparse superposition codes were recently introduced by Barron and Joseph for reliable communication over the AWGN channel at rates approaching the channel capacity. The codebook is defined in terms of a Gaussian design matrix, and codewords…

Information Theory · Computer Science 2017-03-14 Cynthia Rush , Adam Greig , Ramji Venkataramanan

Recently, it has been observed that terminated low-density-parity-check (LDPC) convolutional codes (or spatially-coupled codes) appear to approach capacity universally across the class of binary memoryless channels. This is facilitated by…

Information Theory · Computer Science 2011-10-12 Phong S. Nguyen , Arvind Yedla , Henry D. Pfister , Krishna R. Narayanan

In this paper, we prove that the sub-field images of generalized Reed-Solomon (RS) codes can achieve the symmetric capacity of p-ary memoryless channels. Unlike the totally random linear code ensemble, as a class of maximum distance…

Information Theory · Computer Science 2025-05-14 Xiangping Zheng , Xiao Ma

We present a comprehensive framework for structured sparse coding and modeling extending the recent ideas of using learnable fast regressors to approximate exact sparse codes. For this purpose, we develop a novel block-coordinate proximal…

Machine Learning · Computer Science 2012-06-22 Alex Bronstein , Pablo Sprechmann , Guillermo Sapiro

This paper studies a generalization of sparse superposition codes (SPARCs) for communication over the complex additive white Gaussian noise (AWGN) channel. In a SPARC, the codebook is defined in terms of a design matrix, and each codeword…

Information Theory · Computer Science 2021-06-25 Kuan Hsieh , Ramji Venkataramanan

This paper investigates the belief propagation decoding of spatially-coupled MacKay-Neal (SC-MN) codes over erasure channels with memory. We show that SC-MN codes with bounded degree universally achieve the symmetric information rate (SIR)…

Information Theory · Computer Science 2015-01-28 Masaru Fukushima , Takuya Okazaki , Kenta Kasai

A rateless code encodes a finite length information word into an infinitely long codeword such that longer prefixes of the codeword can tolerate a larger fraction of errors. A rateless code achieves capacity for a family of channels if, for…

Information Theory · Computer Science 2014-06-03 Benny Applebaum , Liron David , Guy Even

We survey coding techniques that enable reliable transmission at rates that approach the capacity of an arbitrary discrete memoryless channel. In particular, we take the point of view of modern coding theory and discuss how recent advances…

Information Theory · Computer Science 2018-01-04 Marco Mondelli , S. Hamed Hassani , Rüdiger Urbanke

Consider communication over a binary-input memoryless output-symmetric channel with low density parity check (LDPC) codes and maximum a posteriori (MAP) decoding. The replica method of spin glass theory allows to conjecture an analytic…

Information Theory · Computer Science 2016-11-17 Shrinivas Kudekar , Nicolas Macris

Low-density parity-check (LDPC) convolutional codes have been shown to exhibit excellent performance under low-complexity belief-propagation decoding [1], [2]. This phenomenon is now termed threshold saturation via spatial coupling. The…

Information Theory · Computer Science 2013-12-30 Santhosh Kumar , Andrew J. Young , Nicolas Macris , Henry D. Pfister

This paper presents a new class of sparse superposition codes for low-rates and short-packet communications over the additive white Gaussian noise channel. The new code is orthogonal sparse superposition (OSS) code. A codeword of OSS codes…

Information Theory · Computer Science 2020-11-24 Yunseo Nam , Jeonghun Park , Songnam Hong , Namyoon Lee

This paper is concerned with a class of low density generator matrix codes (LDGM), called repetition and superposition (RaS) codes, which have been proved to be capacity-achieving over binary-input output-symmetric (BIOS) channels in terms…

Information Theory · Computer Science 2024-02-22 Yixin Wang , Xiao Ma

We consider the discrete memoryless asymmetric broadcast channels. We prove that the error probability of decoding tends to one exponentially for rates outside the capacity region and derive an explicit lower bound of this exponent…

Information Theory · Computer Science 2020-08-11 Yasutada Oohama

We study the approximate message-passing decoder for sparse superposition coding on the additive white Gaussian noise channel and extend our preliminary work [1]. We use heuristic statistical-physics-based tools such as the cavity and the…

Information Theory · Computer Science 2017-07-17 Jean Barbier , Florent Krzakala

This work develops a rate-distortion-based approach to stochastic Chase decoding of algebraic codes over binary memoryless symmetric (BMS) channels, replacing the heuristics traditionally used to determine flip probabilities with…

Information Theory · Computer Science 2026-05-20 Amit Berman , Ariel Doubchak , Uri Erez , Tal Philosof , Ilya Shapir

Sparse regression codes (SPARCs) are a class of codes that encode information through the superposition of columns of a randomised coding matrix. The combination with an outer non-binary low density parity check (NB-LDPC) code was recently…

Information Theory · Computer Science 2025-09-23 Alexander Fengler , Burak Çakmak , Giuseppe Caire

Spatially coupled codes have been of interest recently owing to their superior performance over memoryless binary-input channels. The performance is good both asymptotically, since the belief propagation thresholds approach capacity, as…

Information Theory · Computer Science 2012-12-03 Aravind R. Iyengar , Paul H. Siegel , Rudiger L. Urbanke , Jack K. Wolf

We prove that, for all binary-input symmetric memoryless channels, polar codes enable reliable communication at rates within $\epsilon > 0$ of the Shannon capacity with a block length, construction complexity, and decoding complexity all…

Information Theory · Computer Science 2013-11-19 Venkatesan Guruswami , Patrick Xia