Detection over Rapidly Changing Communication Channels Using Deep Learning

Nariman Farsad, Andrea Goldsmith

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

Abstract

In this work, we demonstrate that deep learning algorithms can be trained for detection in rapidly changing channels, without any knowledge about the underlying channel models. This is achieved by training a deep learning algorithm called the sliding bidirectional recurrent neural network (SBRNN). The algorithm is trained using a dataset that is generated by transmitting a modulated data sequence (symbol sequence) over a parameterized Poisson channel, where the channel parameters are constant over each training sequence and then change for the next sequence. We show that although the SBRNN algorithm is trained using data where the channel state information (CSI) is constant over each sequence of symbols, it still performs well in rapidly changing channels, where the CSI changes on the order of the symbol duration.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages604-608
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Externally publishedYes
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period10/28/1810/31/18

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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