Joint Channel Coding and Modulation via Deep Learning

Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Channel coding and modulation are two fundamental building blocks of physical layer wireless communications. We propose a neural network based end-To-end communication system, where both the channel coding and the modulation blocks are modeled as neural networks. Our proposed architecture combines Turbo Autoencoder together with feed-forward neural networks for modulation, and hence called TurboAE-MOD. Turbo Autoencoder was introduced in [1] and consists of a neural network based channel encoder (convolutional neural networks with an interleaver) and a neural network based decoder (iterations of convolutional neural networks with interleavers and de-interleavers in between). By allowing joint training of the channel coding and modulation in an end-To-end manner, we demonstrate that TurboAE-MOD performs comparable to modern codes stacked with canonical modulations for moderate block lengths. We also demonstrate that TurboAE-MOD learns interesting modulation patterns that are amenable to meaningful interpretations.

Original languageEnglish (US)
Title of host publication2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154787
DOIs
StatePublished - May 2020
Event21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020 - Atlanta, United States
Duration: May 26 2020May 29 2020

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2020-May

Conference

Conference21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
CountryUnited States
CityAtlanta
Period5/26/205/29/20

Keywords

  • Autoencoder
  • Channel Coding
  • Deep Learning
  • Modulation
  • Turbo Principle

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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