English
Related papers

Related papers: New Techniques for Upper-Bounding the ML Decoding …

200 papers

A linear time approximate maximum likelihood decoding algorithm on tail-biting trellises is prsented, that requires exactly two rounds on the trellis. This is an adaptation of an algorithm proposed earlier with the advantage that it reduces…

Information Theory · Computer Science 2008-02-07 K. Murali Krishnan , Priti Shankar

In this paper, we perform a threshold analysis of braided convolutional codes (BCCs) on the additive white Gaussian noise (AWGN) channel. The decoding thresholds are estimated by Monte-Carlo density evolution (MC-DE) techniques and compared…

Information Theory · Computer Science 2018-03-01 Muhammad Umar Farooq , Saeedeh Moloudi , Michael Lentmaier

Recently, a new class of binary codes for overloaded CDMA systems are proposed that not only has the ability of errorless communication but also suitable for detecting active users. These codes are called COWDA [1]. In [1], a Maximum…

Information Theory · Computer Science 2010-01-26 Ali Mousavi , Pedram Pad , Farokh Marvasti

In this paper, for the purposes of information transmission and network error correction simultaneously, three classes of important linear network codes in network coding, linear multicast/broadcast/dispersion codes are generalized to…

Information Theory · Computer Science 2013-02-19 Xuan Guang , Fang-Wei Fu

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

Binary optimization is a central problem in mathematical optimization and its applications are abundant. To solve this problem, we propose a new class of continuous optimization techniques which is based on Mathematical Programming with…

Optimization and Control · Mathematics 2017-12-07 Ganzhao Yuan , Bernard Ghanem

Binary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale systems. However, training…

Quantum Physics · Physics 2025-11-18 Luca Nepote , Alix Lhéritier , Nicolas Bondoux , Marios Kountouris , Maurizio Filippone

Lagrangian relaxation stands among the most efficient approaches for solving a Mixed Integer Linear Programs (MILP) with difficult constraints. Given any duals for these constraints, called Lagrangian Multipliers (LMs), it returns a bound…

Machine Learning · Computer Science 2024-10-21 Francesco Demelas , Joseph Le Roux , Mathieu Lacroix , Axel Parmentier

In this paper we present a new bound obtained with the probabilistic method for the solution of the Set Covering problem with unit costs. The bound is valid for problems of fixed dimension, thus extending previous similar asymptotic…

Combinatorics · Mathematics 2014-07-18 Giovanni Felici , Sokol Ndreca , Aldo Procacci , Benedetto Scoppola

In this paper, we study binary constrained codes that are resilient to bit-flip errors and erasures. In our first approach, we compute the sizes of constrained subcodes of linear codes. Since there exist well-known linear codes that achieve…

Information Theory · Computer Science 2023-04-20 V. Arvind Rameshwar , Navin Kashyap

Over binary input channels, uniform distribution is a universal prior, in the sense that it allows to maximize the worst case mutual information over all binary input channels, ensuring at least 94.2% of the capacity. In this paper, we…

Information Theory · Computer Science 2010-04-28 Emmanuel Abbe , Rethnakaran Pulikkoonattu

Motivated by a wide-spread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which also…

Information Theory · Computer Science 2010-04-16 Sergey Loyka , Francois Gagnon , Victoria Kostina

In this paper, we distinguish two guessing algorithms for decoding binary linear codes. One is the guessing noise decoding (GND) algorithm, and the other is the guessing codeword decoding (GCD) algorithm. We prove that the GCD is a maximum…

Information Theory · Computer Science 2024-01-31 Xiao Ma

Universally achievable error exponents pertaining to certain families of channels (most notably, discrete memoryless channels (DMC's)), and various ensembles of random codes, are studied by combining the competitive minimax approach,…

Information Theory · Computer Science 2007-08-01 Yaniv Akirav , Neri Merhav

Linear programming (LP) decoding approximates maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP problem. The LP problem is defined by a set of…

Information Theory · Computer Science 2013-01-01 Xiaojie Zhang , Paul H. Siegel

The minimum weight perfect matching (MWPM) decoder is the standard decoding strategy for quantum surface codes. However, it suffers a harsh decrease in performance when subjected to biased or non-identical quantum noise. In this work, we…

Currently known secondary construction techniques for linear codes mainly include puncturing, shortening, and extending. In this paper, we propose a novel method for the secondary construction of linear codes based on their weight…

Information Theory · Computer Science 2025-11-25 Dongmei Huang , Qunying Liao , Sihem Mesnager , Gaohua Tang , Haode Yan

Polar codes are of great interest since they are the first provably capacity-achieving forward error correction codes. To improve throughput and to reduce decoding latency of polar decoders, maximum likelihood (ML) decoding units are used…

Information Theory · Computer Science 2015-10-27 Chenrong Xiong , Jun Lin , Zhiyuan Yan

We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show that approximate maximum likelihood learning of model…

Machine Learning · Statistics 2021-02-17 Artem Artemev , David R. Burt , Mark van der Wilk

In this paper, we address the design of high spectral-efficiency Barnes-Wall (BW) lattice codes which are amenable to low-complexity decoding in additive white Gaussian noise (AWGN) channels. We propose a new method of constructing complex…

Information Theory · Computer Science 2013-01-09 J. Harshan , Emanuele Viterbo , Jean-Claude Belfiore