Deep Learning-Aided Spatial Multiplexing with Index Modulation

Merve Turhan*, Ersin Öztürk, Hakan Ali Çırpan

*Corresponding author for this work

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

Abstract

In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblock-based detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.

Original languageEnglish
Title of host publicationMachine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers
EditorsÉric Renault, Selma Boumerdassi, Paul Mühlethaler
PublisherSpringer Science and Business Media Deutschland GmbH
Pages226-236
Number of pages11
ISBN (Print)9783030708658
DOIs
Publication statusPublished - 2021
Event3rd International Conference on Machine Learning for Networking, MLN 2020 - Paris, France
Duration: 24 Nov 202026 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12629 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Machine Learning for Networking, MLN 2020
Country/TerritoryFrance
CityParis
Period24/11/2026/11/20

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Deep learning
  • GFDM
  • Index modulation
  • OFDM
  • Spatial multiplexing

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