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Data-parallel SGD is the de facto algorithm for distributed optimization, especially for large scale machine learning. Despite its merits, communication bottleneck is one of its persistent issues. Most compression schemes to alleviate this…

Neural and Evolutionary Computing · Computer Science 2024-02-07 Ashok Vardhan Makkuva , Marco Bondaschi , Thijs Vogels , Martin Jaggi , Hyeji Kim , Michael C. Gastpar

We study the problem of inferring a sparse vector from random linear combinations of its components. We propose the Accelerated Orthogonal Least-Squares (AOLS) algorithm that improves performance of the well-known Orthogonal Least-Squares…

Machine Learning · Statistics 2018-04-17 Abolfazl Hashemi , Haris Vikalo

Modern applications are driving demand for ultra-reliable low-latency communications, rekindling interest in the performance of short, high-rate error correcting codes. To that end, here we introduce a soft-detection variant of Guessing…

Information Theory · Computer Science 2021-06-16 Ken R. Duffy

We present a new approach for solving (minimum disagreement) correlation clustering that results in sublinear algorithms with highly efficient time and space complexity for this problem. In particular, we obtain the following algorithms for…

Data Structures and Algorithms · Computer Science 2021-09-30 Sepehr Assadi , Chen Wang

Being an effective non-orthogonal multiple access (NOMA) technique, sparse code multiple access (SCMA) is promising for future wireless communication. Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance and lower…

Information Theory · Computer Science 2018-04-03 Chuan Zhang , Chao Yang , Wei Xu , Shunqing Zhang , Zaichen Zhang , Xiaohu You

We study the problem of recovering sparse signals from compressed linear measurements. This problem, often referred to as sparse recovery or sparse reconstruction, has generated a great deal of interest in recent years. To recover the…

Methodology · Statistics 2016-01-01 Jian Wang , Ping Li

Future beyond-5G and 6G systems demand ultra-reliable, low-latency communication with short blocklengths, motivating the development of universal decoding algorithms. Guessing decoding, which infers the noise or codeword candidate in order…

Information Theory · Computer Science 2025-11-24 Qianfan Wang , Jifan Liang , Peihong Yuan , Ken R. Duffy , Muriel Médard , Xiao Ma

Recently, it was found that clipping can significantly improve the section error rate (SER) performance of sparse regression (SR) codes if an optimal clipping threshold is chosen. In this paper, we propose irregularly clipped SR codes,…

Information Theory · Computer Science 2021-06-04 Wencong Li , Lei Liu , Brian M. Kurkoski

In this paper, we propose a fast decoder algorithm for uniquely decodable (errorless) code sets for overloaded synchronous optical code-division multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a way that the…

Information Theory · Computer Science 2019-02-27 Michel Kulhandjian , Hovannes Kulhandjian , Claude D'Amours , Halim Yanikomeroglu , Gurgen Khachatrian

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

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an…

Information Theory · Computer Science 2018-08-02 Hyoungju Ji , Sunho Park , Byonghyo Shim

In this paper, we propose a new linear-equation ordered-statistics decoding (LE-OSD). Unlike the OSD, LE-OSD uses high reliable parity bits rather than information bits to recover the codeword estimates, which is equivalent to solving a…

Information Theory · Computer Science 2021-10-25 Chentao Yue , Mahyar Shirvanimoghaddam , Giyoon Park , Ok-Sun Park , Branka Vucetic , Yonghui Li

Though the method of least squares has been used for a long time in solving signal processing problems, in the recent field of sparse recovery from compressed measurements, this method has not been given much attention. In this paper we…

Information Theory · Computer Science 2016-08-01 Samrat Mukhopadhyay , Prateek Vashishtha and , Mrityunjoy Chakraborty

Error correction code is a major part of the communication physical layer, ensuring the reliable transfer of data over noisy channels. Recently, neural decoders were shown to outperform classical decoding techniques. However, the existing…

Machine Learning · Computer Science 2022-03-30 Yoni Choukroun , Lior Wolf

A superimposed code is a collection of binary vectors (codewords) with the property that no vector is contained in the Boolean sum of any $k$ others, enabling unique identification of codewords within any group of $k$. Superimposed codes…

Data Structures and Algorithms · Computer Science 2025-08-05 Gianluca De Marco , Dariusz R. Kowalski

In this work, we consider efficient maximum-likelihood decoding of linear block codes for small-to-moderate block lengths. The presented approach is a branch-and-bound algorithm using the cutting-plane approach of Zhang and Siegel (IEEE…

Information Theory · Computer Science 2014-04-29 Michael Helmling , Eirik Rosnes , Stefan Ruzika , Stefan Scholl

We study uniquely decodable codes and list decodable codes in the high-noise regime, specifically codes that are uniquely decodable from $\frac{1-\varepsilon}{2}$ fraction of errors and list decodable from $1-\varepsilon$ fraction of…

Information Theory · Computer Science 2024-11-06 Xin Li , Songtao Mao

A new channel coding approach was proposed in [1] for random multiple access communication over the discrete-time memoryless channel. The coding approach allows users to choose their communication rates independently without sharing the…

Information Theory · Computer Science 2016-11-15 Zheng Wang , Jie Luo

Superposition codes are efficient for the Additive White Gaussian Noise channel. We provide here a replica analysis of the performances of these codes for large signals. We also consider a Bayesian Approximate Message Passing decoder based…

Information Theory · Computer Science 2014-10-03 Jean Barbier , Florent Krzakala

In this paper, we propose a novel transmission scheme, called sparse layered MIMO (SL-MIMO), that combines non-orthogonal transmission and singular value decomposition (SVD) precoding. Nonorthogonality in SL-MIMO allows re-using of the…

Information Theory · Computer Science 2022-08-15 Mohamad H. Dinan , Nemanja Stefan Perovic , Mark F. Flanagan