Situation-Aware Channel Covariance Prediction for Deep Learning Aided Massive MIMO Systems

Abdelrahman Taha, Ahmed Alkhateeb

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

Abstract

Designing efficient massive MIMO systems operating in a frequency-division duplexing (FDD) mode is one of the main intriguing research directions in the last decade. One of the main design challenges is reducing the huge training overhead incurred from acquiring the downlink channel knowledge at the base station. This challenge is even more prominent when serving highly-mobile users with high levels of location uncertainty. In this paper, we propose a novel situation-aware channel covariance prediction solution for downlink beamforming design. The proposed solution acquires imperfect knowledge of uplink and downlink channels and user location in the learning phase. In the operation phase, the proposed solution acquires only uplink channel estimates to predict a denoised location, which is then used to predict the downlink channel covariance matrix, for downlink beamforming design. Simulation results show the pro-posed solution achieves robust performance against uncertainty in the location information and imperfection in the downlink channel knowledge, both acquired in the learning phase, which makes it promising for supporting highly-mobile applications.

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
Pages1342-1346
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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