Learning optimal lattice codes for MIMO communications

Laia Amorós, Mikko Pitkänen

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

1 Citation (Scopus)

Abstract

We propose a novel reinforcement learning approach to learning lattice codes for MIMO channels. We use the block error rate as a loss function to be minimized and compare the learnt lattices with those obtained from algebraic design methods for different SNR ranges. Our results indicate that our learnt lattices achieve close to optimal performance in some cases.

Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages2960-2964
Number of pages5
Volume2021-June
ISBN (Electronic)978-1-7281-7605-5
DOIs
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Virtua, Online, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Publication series

Name Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritoryCanada
CityToronto
Period06/06/202111/06/2021

Keywords

  • Coding theory
  • Lattices
  • MIMO channels
  • Physical layer security
  • Reinforcement learning

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