Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems

Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb

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

Abstract

Deploying large scale antenna arrays is a key characteristic of current and future wireless communication systems. However, due to some non-ideal practical conditions, such as the unknown array geometry or possible hardware impairments, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of the large-scale MIMO systems or to combat the high path-loss in millimeter wave (mmWave) communications. In this paper, we propose a novel solution that leverages deep reinforcement learning (DRL) to learn the beam pattern that is optimized for a group of users without the explicit knowledge of the channels. Simulation results show that the developed solution is capable of finding the near optimal beam pattern with quantized phase shifters and with only requiring the beamforming gain feedback from the users.

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
Pages445-449
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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