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

Author:   Bikash Kumar Dey ,  Sidharth Jaggi ,  Michael Langberg ,  Anand D. Sarwate
Publisher:   now publishers Inc
ISBN:  

9781638284604


Pages:   306
Publication Date:   03 December 2024
Format:   Paperback
Availability:   In Print   Availability explained
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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.

Full Product Details

Author:   Bikash Kumar Dey ,  Sidharth Jaggi ,  Michael Langberg ,  Anand D. Sarwate
Publisher:   now publishers Inc
Imprint:   now publishers Inc
Weight:   0.433kg
ISBN:  

9781638284604


ISBN 10:   1638284601
Pages:   306
Publication Date:   03 December 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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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