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

Ran Tamir, Neri Merhav

Research output: Contribution to journalArticlepeer-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 both the error exponent of the TRC and the expurgated exponent strictly improve upon the random coding exponent, at relatively low coding rates, which is a known fact for discrete memoryless channels without random states. We also show 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
Pages (from-to)7479-7498
Number of pages20
JournalIEEE Transactions on Information Theory
Volume69
Issue number12
DOIs
StateAccepted/In press - 2023

Keywords

  • Channel models
  • Codes
  • Decoding
  • Dirty-paper channel
  • Encoding
  • Error probability
  • Gel’fand-Pinsker Channel
  • Monte Carlo methods
  • Optimization
  • error exponent
  • expurgated exponent
  • random states
  • side information
  • typical random code

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences
  • Computer Science Applications

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