Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

Junghoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton

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

Abstract

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the practical setting where (i) the IRS reflection coefficients are attained by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station (BS) to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not practical in this setting due to the difficulty of channel estimation and the low data rate of the feedback channel. To address these challenges, we develop a novel adaptive codebook-based limited feedback protocol to control the IRS. We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC), which is based on a deep reinforcement learning. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.

Original languageEnglish (US)
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5171-5177
Number of pages7
ISBN (Electronic)9781538683477
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: May 16 2022May 20 2022

Publication series

NameIEEE International Conference on Communications
Volume2022-May
ISSN (Print)1550-3607

Conference

Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period5/16/225/20/22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks'. Together they form a unique fingerprint.

Cite this