Channel Input Adaptation via Natural Type Selection

Sergey Tridenski, Ram Zamir

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


We propose an on-line algorithm for adapting the input of an unknown or slowly varying channel, while keeping reliable communication at some fixed rate R during the adaptation process. The purpose of the algorithm is to push the generating distribution of an i.i.d. random code toward the input that achieves the channel capacity C. The algorithm uses one bit feedback per each transmission block, that acknowledges whether the decoded codeword crossed some pre-determined threshold T > R, with respect to some 'fitness' metric. In the rare event of threshold crossing, the encoder and decoder update the input distribution according to the type of the current codeword, while the decoder updates the fitness metric. We show that for a large block length, this algorithm simulates computation of the channel correct-decoding exponent, and it leads to the capacity-achieving input if we set T = C.1

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538647806
StatePublished - 15 Aug 2018
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: 17 Jun 201822 Jun 2018

Publication series

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


Conference2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/TerritoryUnited States


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