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We propose an approximation of maximum-likelihood detection in ISI channels based on linear programming or message passing. We convert the detection problem into a binary decoding problem, which can be easily combined with LDPC decoding. We…

Information Theory · Computer Science 2016-11-18 Mohammad H. Taghavi , Paul H. Siegel

Error-correcting codes are one of the most fundamental objects in pseudorandomness, with applications in communication, complexity theory, and beyond. Codes are useful because of their ability to support decoding, which is the task of…

Information Theory · Computer Science 2024-08-28 Shashank Srivastava

This paper proposes a novel maximum-likelihood (ML) soft-decision decoding framework for linear block codes, termed error-building decoding (EBD). The complete decoding process can be performed using only the parity-check matrix, without…

Information Theory · Computer Science 2026-01-06 Guoda Qiu , Ling Liu , Yuejun Wei , Liping Li

A new class of folded subspace codes for noncoherent network coding is presented. The codes can correct insertions and deletions beyond the unique decoding radius for any code rate $R\in[0,1]$. An efficient interpolation-based decoding…

Information Theory · Computer Science 2015-04-22 Hannes Bartz , Vladimir Sidorenko

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm remain unclear. Previous works only compare decoding…

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

Some new results are derived concerning random coding error exponents and expurgated exponents for list decoding with a deterministic list size $L$. Two asymptotic regimes are considered, the fixed list-size regime, where $L$ is fixed…

Information Theory · Computer Science 2016-11-17 Neri Merhav

Recent developments in decoding of Tanner codes with maximum-likelihood certificates are based on a sufficient condition called local-optimality. We define hierarchies of locally-optimal codewords with respect to two parameters. One…

Information Theory · Computer Science 2012-08-15 Nissim Halabi , Guy Even

We show that the binary Golay code is slightly more power efficient than the extended binary Golay code under maximum-likelihood (ML), hard-decision decoding. In fact, if a codeword from the extended code is transmitted, one cannot achieve…

Information Theory · Computer Science 2016-02-19 Jon Hamkins

Spinal codes are a type of capacity-achieving rateless codes that have been proved to approach the Shannon capacity over the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC). In this paper, we aim to…

Information Theory · Computer Science 2022-04-05 Aimin Li , Shaohua Wu , Jian Jiao , Ning Zhang , Qinyu Zhang

Currently for the nt x nr MIMO channel, any explicitly constructed space-time (ST) designs that achieve optimality with respect to the diversity multiplexing tradeoff (DMT) are known to do so only when decoded using maximum likelihood (ML)…

Information Theory · Computer Science 2009-01-28 Joakim Jalden , Petros Elia

Based on the notion of supercodes, we propose a two-phase maximum-likelihood soft-decision decoding (tpMLSD) algorithm for binary linear block codes in this work. The first phase applies the Viterbi algorithm backwardly to a trellis derived…

Information Theory · Computer Science 2014-08-07 Yunghsiang S. Han , Hung-Ta Pai , Po-Ning Chen , Ting-Yi Wu

We propose Dijkstra's algorithm with bounded list size after QR decomposition for decreasing the computational complexity of near maximum-likelihood (ML) detection of signals over multiple-input-multiple-output (MIMO) channels. After that,…

Information Theory · Computer Science 2008-09-02 Atsushi Okawado , Ryutaroh Matsumoto , Tomohiko Uyematsu

The decoding error probability of codes is studied as a function of their block length. It is shown that the existence of codes with a polynomially small decoding error probability implies the existence of codes with an exponentially small…

Information Theory · Computer Science 2007-07-13 Alexei Ashikhmin , Vitaly Skachek

We propose a novel optimization-based decoding algorithm for LDPC-coded massive MIMO channels. The proposed decoding algorithm is based on a proximal gradient method for solving an approximate maximum a posteriori (MAP) decoding problem.…

Information Theory · Computer Science 2021-02-11 Tadashi Wadayama , Satoshi Takabe

This paper studies channel coding for the discrete memoryless multiple-access channel with a given (possibly suboptimal) decoding rule. A multi-letter successive decoding rule depending on an arbitrary non-negative decoding metric is…

Information Theory · Computer Science 2017-12-08 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

An efficient decoder for the generalized first-order Reed-Muller code RM_q(1,m) is essential for the decoding of various block-coding schemes for orthogonal frequency-division multiplexing with reduced peak-to-mean power ratio. We present…

Information Theory · Computer Science 2007-07-16 Kai-Uwe Schmidt , Adolf Finger

Many probabilistic inference tasks involve summations over exponentially large sets. Recently, it has been shown that these problems can be reduced to solving a polynomial number of MAP inference queries for a model augmented with randomly…

Artificial Intelligence · Computer Science 2013-09-27 Stefano Ermon , Carla P. Gomes , Ashish Sabharwal , Bart Selman

The error-correcting pair is a general algebraic decoding method for linear codes. The near maximal distance separable (NMDS) linear code is a subclass of linear codes and has applications in secret sharing scheme and communication systems…

Information Theory · Computer Science 2025-06-10 Dong He , Zhaohui Zhang , Qunying Liao

In this paper we explore the decision regions of Linear Programming (LP) decoding. We compare the decision regions of an LP decoder, a Belief Propagation (BP) decoder and the optimal Maximum Likelihood (ML) decoder. We study the effect of…

Information Theory · Computer Science 2011-06-15 Asi Lifshitz , Yair Be'ery