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Reed--Muller (RM) codes are known to achieve capacity on binary symmetric channels (BSC) under the Maximum a Posteriori (MAP) decoder. However, it remains an open problem to design a capacity achieving polynomial-time RM decoder. Due to a…

Information Theory · Computer Science 2025-05-05 Xuzhe Xia , Nicholas Kwan , Lele Wang

This paper considers the problem of covert communication with mismatched decoding, in which a sender wishes to reliably communicate with a receiver whose decoder is fixed and possibly sub-optimal, and simultaneously to ensure that the…

Information Theory · Computer Science 2021-11-11 Qiaosheng Zhang , Vincent Y. F. Tan

It has been known for a long time that the mutual information between the input sequence and output of a binary symmetric channel (BSC) is upper bounded by the mutual information between the same input sequence and the output of a binary…

Information Theory · Computer Science 2024-01-29 Uri Erez , Or Ordentlich , Shlomo Shamai

We study the capacity regions of broadcast channels with binary inputs and symmetric outputs. We study the partial order induced by the more capable ordering of broadcast channels for channels belonging to this class. This study leads to…

Information Theory · Computer Science 2010-01-14 Yanlin Geng , Chandra Nair , Shlomo Shamai , Zizhou Vincent Wang

This work introduces the particle-intensity channel (PIC) as a model for molecular communication systems and characterizes the properties of the optimal input distribution and the capacity limits for this system. In the PIC, the transmitter…

Information Theory · Computer Science 2017-05-24 Nariman Farsad , Christopher Rose , Muriel Médard , Andrea Goldsmith

We introduce a new approach to proving that a sequence of deterministic linear codes achieves capacity on an erasure channel under maximum a posteriori decoding. Rather than relying on the precise structure of the codes our method exploits…

Information Theory · Computer Science 2016-01-19 Shrinivas Kudekar , Santhosh Kumar , Marco Mondelli , Henry D. Pfister , Eren Şaşoğlu , Rüdiger Urbanke

A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual…

Information Theory · Computer Science 2019-08-23 Wenyi Zhang , Yizhu Wang , Cong Shen , Ning Liang

In this paper, we consider quantized decoding of LDPC codes on the binary symmetric channel. The binary message passing algorithms, while allowing extremely fast hardware implementation, are not very attractive from the perspective of…

Information Theory · Computer Science 2016-11-17 Lucile Sassatelli , Shashi Kiran Chilappagari , Bane Vasic , David Declercq

We consider the problem of block-coded communication, where in each block, the channel law belongs to one of two disjoint sets. The decoder is aimed to decode only messages that have undergone a channel from one of the sets, and thus has to…

Information Theory · Computer Science 2015-09-08 Nir Weinberger , Neri Merhav

We present an algorithm that, given a channel, determines if there is a distance for it such that the maximum likelihood decoder coincides with the minimum distance decoder. We also show that any metric, up to a decoding equivalence, can be…

Information Theory · Computer Science 2016-02-26 Rafael G. L. D'Oliveira , Marcelo Firer

This is a three part paper. Optimality of source-channel separation for communication with a fidelity criterion when the channel is compound as defined by Csiszar and Korner in their book and general as defined by Verdu and Han, is proved…

Information Theory · Computer Science 2013-02-26 Mukul Agarwal , Sanjoy Mitter , Anant Sahai

The compute-and-forward framework permits each receiver in a Gaussian network to directly decode a linear combination of the transmitted messages. The resulting linear combinations can then be employed as an end-to-end communication…

Information Theory · Computer Science 2016-11-17 Bobak Nazer , Viveck Cadambe , Vasilis Ntranos , Giuseppe Caire

We consider the problem of compressing memoryless binary data with or without side information at the decoder. We review the parity- and the syndrome-based approaches and discuss their theoretical limits, assuming that there exists a…

Information Theory · Computer Science 2010-08-03 Lorenzo Cappellari , Andrea De Giusti

In this paper, new techniques are presented to either simplify or improve most existing upper bounds on the maximum-likelihood (ML) decoding performance of the binary linear codes over additive white Gaussian noise (AWGN) channels. Firstly,…

Information Theory · Computer Science 2015-03-19 Xiao Ma , Jia Liu , Baoming Bai

The performance of maximum-likelihood (ML) decoded binary linear block codes is addressed via the derivation of tightened upper bounds on their decoding error probability. The upper bounds on the block and bit error probabilities are valid…

Information Theory · Computer Science 2007-07-13 M. Twitto , I. Sason , S. Shamai

Uncertainty quantification requires efficient summarization of high- or even infinite-dimensional (i.e., non-parametric) distributions based on, e.g., suitable point estimates (modes) for posterior distributions arising from model-specific…

Statistics Theory · Mathematics 2024-04-10 Christian Clason , Tapio Helin , Remo Kretschmann , Petteri Piiroinen

We introduce a new analysis technique to derive a single-letter upper bound on the mismatch capacity of a stationary, single-user, memoryless channel with a decoding metric $q$. Our bound is obtained by considering a multicast transmission…

Information Theory · Computer Science 2021-03-29 Anelia Somekh-Baruch

We consider a channel with a binary input X being corrupted by a continuous-valued noise that results in a continuous-valued output Y. An optimal binary quantizer is used to quantize the continuous-valued output Y to the final binary output…

Signal Processing · Electrical Eng. & Systems 2020-01-08 Thuan Nguyen , Thinh Nguyen

Bayesian statistics has two common measures of central tendency of a posterior distribution: posterior means and Maximum A Posteriori (MAP) estimates. In this paper, we discuss a connection between MAP estimates and posterior means. We…

Statistics Theory · Mathematics 2024-10-02 Michiko Okudo , Keisuke Yano

We examine the concentration of uniform generalization errors around their expectation in binary linear classification problems via an isoperimetric argument. In particular, we establish Poincar\'{e} and log-Sobolev inequalities for the…

Machine Learning · Statistics 2025-06-27 Shogo Nakakita