Deep MIMO detection

Neev Samuel, Tzvi Diskin, Ami Wiesel

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

293 Scopus citations

Abstract

In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.

Original languageAmerican English
Title of host publication18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781509030088
DOIs
StatePublished - 19 Dec 2017
Event18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 - Sapporo, Japan
Duration: 3 Jul 20176 Jul 2017

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2017-July

Conference

Conference18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Country/TerritoryJapan
CitySapporo
Period3/07/176/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Deep Learning
  • MIMO Detection
  • Neural Networks

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