Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation

Abdelrahman Taha, Yu Zhang, Faris B. Mismar, Ahmed Alkhateeb

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

5 Scopus citations

Abstract

The promising coverage and spectral efficiency gains of intelligent reflecting surfaces (IRSs) are attracting increasing interest. To adopt these surfaces in practice, however, several challenges need to be addressed. One of these main challenges is how to configure the reflecting coefficients on these passive surfaces without requiring massive channel estimation or beam training overhead. Earlier work suggested leveraging supervised learning tools to predict the IRS reflection matrices. While this approach has the potential of reducing the beam training overhead, it requires collecting large datasets for training the neural network models. In this paper, we propose a novel deep reinforcement learning framework for predicting the IRS reflection matrices with minimal beam training overhead. Simulation results show that the proposed online learning framework can converge to the optimal rate that assumes perfect channel knowledge. This represents an important step towards realizing a standalone IRS operation, where the surface configures itself without any control from the infrastructure.

Original languageEnglish (US)
Title of host publication2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154787
DOIs
StatePublished - May 2020
Event21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020 - Atlanta, United States
Duration: May 26 2020May 29 2020

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2020-May

Conference

Conference21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
Country/TerritoryUnited States
CityAtlanta
Period5/26/205/29/20

Keywords

  • beamforming
  • deep reinforcement learning
  • intelligent reflecting surface
  • large intelligent surface
  • reconfigurable intelligent surface
  • smart reflect-Array

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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