Error Exponents of the Dirty-Paper and Gel'fand-Pinsker Channels

Ran Tamir, Neri Merhav

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

Abstract

We derive various error exponents for communication channels with random states, which are available non-causally at the encoder only. For both the finite-alphabet Gel'fand-Pinsker channel and its Gaussian counterpart, the dirty-paper channel, we derive random coding exponents, error exponents of the typical random codes (TRCs), and error exponents of expurgated codes. For the two channel models, we analyze some sub-optimal bin-index decoders, which turn out to be asymptotically optimal, at least for the random coding error exponent. For the dirty-paper channel, we show explicitly via a numerical example, that at rates below capacity, the optimal values of the dirty-paper design parameter α in the random coding sense and in the TRC exponent sense are different from one another, and they are both different from the optimal α that is required for attaining the channel capacity. For the Gel'fand-Pinsker channel, we allow for a variable-rate random binning code construction, and prove that the previously proposed maximum penalized mutual information decoder is asymptotically optimal within a given class of decoders, at least for the random coding error exponent.

Original languageEnglish
Title of host publication2023 IEEE Information Theory Workshop, ITW 2023
Pages272-276
Number of pages5
ISBN (Electronic)9798350301496
DOIs
StatePublished - 2023
Event2023 IEEE Information Theory Workshop, ITW 2023 - Saint-Malo, France
Duration: 23 Apr 202328 Apr 2023

Publication series

Name2023 IEEE Information Theory Workshop, ITW 2023

Conference

Conference2023 IEEE Information Theory Workshop, ITW 2023
Country/TerritoryFrance
CitySaint-Malo
Period23/04/2328/04/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization

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