Deep Learning Based MIMO Channel Prediction: An Initial Proof of Concept Prototype

Jayden Booth, Ahmed Ewaisha, Andreas Spanias, Ahmed Alkhateeb

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

Abstract

Massive MIMO is a key component of current and future wireless communication systems. To harvest the multiplexing and beamforming gains of these large-scale MIMO systems, however, the channel knowledge needs to be acquired at the massive MIMO transmitters. This is typically associated with large training overhead, especially in FDD massive MIMO. Recent research showed that deep learning could lead to interesting gains for massive MIMO systems by mapping the channel knowledge from the uplink to downlink channels or between antennas at nearby locations. In this paper, we provide an initial proof-of-concept prototype for this concept, where we show using a sub-6GHz hardware setup promising results for channel mapping across frequency and space.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages267-271
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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