Adaptive protocols for interactive communication

Shweta Agrawal, Ran Gelles, Amit Sahai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Scopus citations


How much adversarial noise can protocols for interactive communication tolerate? This question was examined by Braverman and Rao (IEEE Trans. Inf. Theory, 2014) for the case of 'robust' protocols, where each party sends messages only in fixed and predetermined rounds. We consider a new class of protocols for interactive communication, which we call adaptive protocols. Such protocols adapt structurally to the noise induced by the channel in the sense that both the order of speaking, and the length of the protocol may vary depending on observed noise. We define models that capture adaptive protocols and study upper and lower bounds on the permissible noise rate in these models. When the length of the protocol may adaptively change according to the noise, we demonstrate a protocol that tolerates noise rates up to 1/3. When the order of speaking may adaptively change as well, we demonstrate a protocol that tolerates noise rates up to 2/3. Hence, adaptivity circumvents an impossibility result of 1/4 on the fraction of tolerable noise (Braverman and Rao, 2014).

Original languageEnglish
Title of host publicationProceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509018062
StatePublished - 10 Aug 2016
Externally publishedYes
Event2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain
Duration: 10 Jul 201615 Jul 2016

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2016 IEEE International Symposium on Information Theory, ISIT 2016

Bibliographical note

Funding Information:
Research supported in part from a DARPA/ONR PROCEED award, NSF Frontier Award 1413955, NSF grants 1228984, 1136174, 1118096, and 1065276, a Xerox Faculty Research Award, a Google Faculty Research Award, an equipment grant from Intel, and an Okawa Foundation Research Grant. This material is based upon work supported by the Defense Advanced Research Projects Agency through the U.S. Office of Naval Research under Contract N00014-11-1-0389. The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense, the National Science Foundation, or the U.S. Government

Publisher Copyright:
© 2016 IEEE.


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