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Motivated by hyper-reliable low-latency communication in 6G, we consider error control coding for short block lengths in multi-antenna fading channels. In general, the channel fading coefficients are unknown at both the transmitter and…

Signal Processing · Electrical Eng. & Systems 2025-05-13 Sai Dinesh Kancharana , Madhusudan Kumar Sinha , Arun Pachai Kannu

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

Sparse regression codes (SPARC) connect the sparse signal recovery framework of compressive sensing with error control coding techniques. SPARC encoding produces codewords which are \emph{sparse} linear combinations of columns of a…

Information Theory · Computer Science 2023-03-27 Madhusudan Kumar Sinha , Arun Pachai Kannu

A joint sparse-regression-code (SPARC) and low-density-parity-check (LDPC) coding scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) is proposed in this paper. Different from the state-of-the-art…

Information Theory · Computer Science 2022-01-12 Tianya Li , Yongpeng Wu , Mengfan Zheng , Dongming Wang , Wenjun Zhang

Belief propagation applied to iterative decoding and sparse recovery through approximate message passing (AMP) are two research areas that have seen monumental progress in recent decades. Inspired by these advances, this article introduces…

Information Theory · Computer Science 2023-01-06 Jamison R. Ebert , Jean-Francois Chamberland , Krishna R. Narayanan

Sparse Regression Codes (SPARCs) are capacity-achieving codes introduced for communication over the Additive White Gaussian Noise (AWGN) channels and were later extended to general memoryless channels. In particular it was shown via…

Information Theory · Computer Science 2024-09-10 Yuhao Liu , Yizhou Xu , Tianqi Hou

Sparse superposition codes, or sparse regression codes (SPARCs), are a recent class of codes for reliable communication over the AWGN channel at rates approaching the channel capacity. Approximate message passing (AMP) decoding, a…

Information Theory · Computer Science 2019-04-24 Cynthia Rush , Ramji Venkataramanan

We consider sparse superposition codes (SPARCs) over complex AWGN channels. Such codes can be efficiently decoded by an approximate message passing (AMP) decoder, whose performance can be predicted via so-called state evolution in the…

Information Theory · Computer Science 2021-03-09 Haiwen Cao , Pascal O. Vontobel

This paper considers the massive MIMO unsourced random access problem in a quasi-static Rayleigh fading setting. The proposed coding scheme is based on a concatenation of a "conventional" channel code (such as, e.g., LDPC) serving as an…

Signal Processing · Electrical Eng. & Systems 2023-05-01 Patrick Agostini , Zoran Utkovski , Slawomir Stanczak

Sparse code multiple access (SCMA) is a promising multiplexing approach to achieve high system capacity. In this paper, we develop a novel iterative detection and decoding scheme for SCMA systems combined with Low-density Parity-check…

Information Theory · Computer Science 2015-08-05 Baicen Xiao , Kexin Xiao , Shutian Zhang , Zhiyong Chen , Bin Xia , Hui Liu

Sparse superposition codes, also called sparse regression codes (SPARCs), are a class of codes for efficient communication over the AWGN channel at rates approaching the channel capacity. In a standard SPARC, codewords are sparse linear…

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

We present an algorithm, dubbed Multi-Branch Matching Pursuit (MBMP), to solve the sparse recovery problem over redundant dictionaries. MBMP combines three different paradigms: being a greedy method, it performs iterative signal support…

Information Theory · Computer Science 2014-07-03 Marco Rossi , Alexander M. Haimovich , Yonina C. Eldar

This paper considers a simple on-off random multiple access channel, where n users communicate simultaneously to a single receiver over m degrees of freedom. Each user transmits with probability lambda, where typically lambda n < m << n,…

Information Theory · Computer Science 2009-03-09 Alyson K. Fletcher , Sundeep Rangan , Vivek K Goyal

This paper proposes a coding framework for capacity-region-achieving sparse regression (SR) codes over MIMO multiple-access channels (MIMO-MAC), where a single SR code is used for each user at the transmitter. With random semi-unitary…

Information Theory · Computer Science 2026-04-14 Hao Yan , Lei Liu , Yuhao Liu , Burak Çakmak , Giuseppe Caire

In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output systems. Our focus is concentrated on efficient decoding for…

Information Theory · Computer Science 2022-08-15 Juntao You , Wenjie Wang , Shansuo Liang , Wei Han , Bo Bai

Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this…

Information Theory · Computer Science 2019-11-05 Ramji Venkataramanan , Sekhar Tatikonda , Andrew Barron

This article introduces a novel concatenated coding scheme called sparse regression LDPC (SR-LDPC) codes. An SR-LDPC code consists of an outer non-binary LDPC code and an inner sparse regression code (SPARC) whose respective field size and…

Information Theory · Computer Science 2024-10-28 Jamison R. Ebert , Jean-Francois Chamberland , Krishna R. Narayanan

Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliability of communications…

Information Theory · Computer Science 2016-11-17 Andrea Tassi , Ioannis Chatzigeorgiou , Daniel E. Lucani

Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number $K_a$ are active on the same time slot. Users employ exactly the same codebook, and the task of…

Information Theory · Computer Science 2021-01-11 Alexander Fengler , Peter Jung , Giuseppe Caire

In this paper, we address the sparse multiple measurement vector (MMV) problem where the objective is to recover a set of sparse nonzero row vectors or indices of a signal matrix from incomplete measurements. Ideally, regardless of the…

Information Theory · Computer Science 2016-01-27 Kyung Su Kim , Sae-Young Chung
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