Data-Driven Polar Codes for Unknown Channels With and Without Memory

Ziv Aharoni, Bashar Huleihel, Henry D. Pfister, Haim H. Permuter

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

Abstract

In this work, a novel data-driven methodology for designing polar codes is proposed. The methodology is suitable for the case where the channel is given as a "black-box"and the designer has access to the channel for generating observations of its inputs and outputs, but does not have access to the explicit channel model. The methodology consists of two components: (1) a neural estimation of the sufficient statistic of the channel outputs using recent advances in Kullback Leibler (KL) estimation, and (2) a neural successive cancellation (NSC) decoder using three neural networks that replace the core elements of the successive cancellation (SC) decoder. The parameters of the neural networks are determined during a training phase where the mutual information of the effective channels is estimated. We demonstrate the performance of the algorithm on memoryless channels and on finite state channels. Then, we compare the results with the optimal decoding given by the SC and SC trellis decoders, respectively.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Information Theory, ISIT 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages1890-1895
Number of pages6
ISBN (Electronic)9781665475549
DOIs
StatePublished - 1 Jan 2023
Event2023 IEEE International Symposium on Information Theory, ISIT 2023 - Taipei, Taiwan, Province of China
Duration: 25 Jun 202330 Jun 2023

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2023-June
ISSN (Print)2157-8095

Conference

Conference2023 IEEE International Symposium on Information Theory, ISIT 2023
Country/TerritoryTaiwan, Province of China
CityTaipei
Period25/06/2330/06/23

Keywords

  • Polar codes
  • channels with memory
  • data-driven

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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