Codes for Adversaries: Between Worst-Case and Average-Case Jamming

Bikash Kumar Dey*, Sidharth Jaggi, Michael Langberg, Anand D. Sarwate, Yihan Zhang

*Corresponding author for this work

Research output: Contribution to journalReview article (Academic Journal)peer-review

Abstract

Over the last 70 years, information theory and coding has enabled communication technologies that have had an astounding impact on our lives. This is possible due to the match between encoding/decoding strategies and corresponding channel models. Traditional studies of channels have taken one of two extremes: Shannon-theoretic models are inherently average-case in which channel noise is governed by a memoryless stochastic process, whereas coding-theoretic (referred to as “Hamming”) models take a worst-case, adversarial, view of the noise. However, for several existing and emerging communication systems the Shannon/average-case view may be too optimistic, whereas the Hamming/worst-case view may be too pessimistic. This monograph takes up the challenge of studying adversarial channel models that lie between the Shannon and Hamming extremes.

Original languageEnglish
Pages (from-to)300-588
Number of pages289
JournalFoundations and Trends in Communications and Information Theory
Volume21
Issue number3-4
DOIs
Publication statusPublished - 3 Dec 2024

Bibliographical note

Publisher Copyright:
©2024 B. K. Dey et al.

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