Variable Length Joint Source-Channel Coding of Text Using Deep Neural Networks

Milind Rao, Nariman Farsad, Andrea Goldsmith

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

2 Scopus citations

Abstract

We consider joint source and channel coding of natural language over a noisy channel using deep learning. While the typical approach based on separate source and channel code design minimizes bit error rates, the proposed deep learning approach preserves semantic information of sentences. In particular, unlike previous work which used a fixed-length encoding per sentence, a variable-length neural network encoder is presented. The performance of this new architecture is compared to the one with fixed-length encoding per sentence. We show that the variable-length encoder has a lower word error rate compared with the fixed-length encoder as well as separate source and channel coding schemes across several different communication channels.

Original languageEnglish (US)
Title of host publication2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538635124
DOIs
StatePublished - Aug 24 2018
Externally publishedYes
Event19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece
Duration: Jun 25 2018Jun 28 2018

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2018-June

Conference

Conference19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Country/TerritoryGreece
CityKalamata
Period6/25/186/28/18

All Science Journal Classification (ASJC) codes

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

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