Stochastic-Adversarial Channels : Online Adversaries With Feedback Snooping
| Autor: | David J. Love, Vinayak Suresh, Eric Ruzomberka |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Node (networking) Computer Science - Information Theory Information Theory (cs.IT) Transmitter Code word 020206 networking & telecommunications 02 engineering and technology Data_CODINGANDINFORMATIONTHEORY Binary erasure channel Binary symmetric channel Encoding (memory) Converse 0202 electrical engineering electronic engineering information engineering business Computer network Communication channel Computer Science::Cryptography and Security Computer Science::Information Theory |
| Zdroj: | ISIT |
| DOI: | 10.48550/arxiv.2104.07194 |
| Popis: | The growing need for reliable communication over untrusted networks has caused a renewed interest in adversarial channel models, which often behave much differently than traditional stochastic channel models. Of particular practical use is the assumption of a \textit{causal} or \textit{online} adversary who is limited to causal knowledge of the transmitted codeword. In this work, we consider stochastic-adversarial mixed noise models. In the set-up considered, a transmit node (Alice) attempts to communicate with a receive node (Bob) over a binary erasure channel (BEC) or binary symmetric channel (BSC) in the presence of an online adversary (Calvin) who can erase or flip up to a certain number of bits at the input of the channel. Calvin knows the encoding scheme and has causal access to Bob's reception through \textit{feedback snooping}. For erasures, we provide a complete capacity characterization with and without transmitter feedback. For bit-flips, we provide interesting converse and achievability bounds. Comment: Extended draft of the conference paper with the same title submitted to the IEEE International Symposium on Information Theory (ISIT) 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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