End-To-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning

Sangwoo Park, Osvaldo Simeone, Joonhyuk Kang

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

1 Scopus citations

Abstract

When a channel model is not available, the end-To-end training of encoder and decoder on a fading noisy channel generally requires the repeated use of the channel and of a feedback link. An important limitation of the approach is that training should be generally carried out from scratch for each new channel. To cope with this problem, prior works considered joint training over multiple channels with the aim of finding a single pair of encoder and decoder that works well on a class of channels. In this paper, we propose to obviate the limitations of joint training via meta-learning. The proposed approach is based on a meta-Training phase in which the online gradient-based meta-learning of the decoder is coupled with the joint training of the encoder via the transmission of pilots and the use of a feedback link. Accounting for channel variations during the meta-Training phase, this work demonstrates the advantages of meta-learning in terms of number of pilots as compared to conventional methods when the feedback link is only available for meta-Training and not at run time.

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
Externally publishedYes
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

All Science Journal Classification (ASJC) codes

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

Keywords

  • Machine learning
  • autoencoder
  • fading channels
  • meta-learning

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