Codes for Adversaries: Between Worst-Case and Average-Case Jamming
Over the last 70 years, information theory and coding have 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.
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Codes for Adversaries: Between Worst-Case and Average-Case Jamming
Over the last 70 years, information theory and coding have 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.
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Codes for Adversaries: Between Worst-Case and Average-Case Jamming

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

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

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

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Overview

Over the last 70 years, information theory and coding have 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.

Product Details

ISBN-13: 9781638284604
Publisher: Now Publishers
Publication date: 12/03/2024
Series: Foundations and Trends(r) in Communications and Information , #67
Pages: 306
Product dimensions: 6.14(w) x 9.21(h) x 0.64(d)

Table of Contents

1. Introduction
2. A Unified Channel Model Using AVCs
3. Motivating Example: Large Alphabets
4. Motivating Example: Binary Erasures
5. List-decoding
6. AVCs with Common Randomness
7. Oblivious Adversaries
8. Omniscient Adversaries
9. Myopic Adversaries
10. Causal (Online) Adversaries
11. Additional Topics and Related Problems
Acknowledgements
References
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