Cooperative Channel Capacity Learning

Nunzio A. Letizia, Andrea M. Tonello, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

In this letter, the problem of determining the capacity of a communication channel is formulated as a cooperative game, between a generator and a discriminator, that is solved via deep learning techniques. The task of the generator is to produce channel input samples for which the discriminator ideally distinguishes conditional from unconditional channel output samples. The learning approach, referred to as cooperative channel capacity learning (CORTICAL), provides both the optimal input signal distribution and the channel capacity estimate. Numerical results demonstrate that the proposed framework learns the capacity-achieving input distribution under challenging non-Shannon settings.

Original languageEnglish (US)
Pages (from-to)1984-1988
Number of pages5
JournalIEEE Communications Letters
Volume27
Issue number8
DOIs
StatePublished - Aug 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Channel capacity
  • capacity learning
  • capacity-achieving distribution
  • deep learning

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