Online Beam Learning for Interference Nulling in Hardware-Constrained mm Wave MIMO Systems

Yu Zhang, Ahmed Alkhateeb

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

1 Scopus citations

Abstract

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. Existing codebooks, however, are typically pre-defined and focus only on improving the beamforming gain of their target user, without taking interference into account, which incurs critical performance degradation. In this paper, we propose an efficient deep reinforcement learning approach that learns how to iteratively optimize the beam pattern to null the interference. The proposed solution achieves that while not requiring any explicit channel knowledge of the desired or interfering users and without requiring any coordination with the interferers. Simulation results show that the developed solution is capable of finding a well-shaped beam pattern that significantly suppresses the interference while sacrificing negligible beamforming/combing gain, highlighting a promising solution for dense mmWave/terahertz networks.

Original languageEnglish (US)
Title of host publication56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages200-204
Number of pages5
ISBN (Electronic)9781665459068
DOIs
StatePublished - 2022
Event56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States
Duration: Oct 31 2022Nov 2 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October
ISSN (Print)1058-6393

Conference

Conference56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/31/2211/2/22

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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