King's College London

Research portal

Energy Efficient Intelligent Reflecting Surface Assisted Terahertz Communications

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publication2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194417
DOIs
PublishedJun 2021
Event2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online
Duration: 14 Jun 202123 Jun 2021

Publication series

Name2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

Conference

Conference2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
CityVirtual, Online
Period14/06/202123/06/2021

Bibliographical note

Publisher Copyright: © 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

This paper investigates the energy efficiency optimisation problem for an intelligent reflecting surface (IRS) assisted Terahertz communication system. In this system, a base station (BS) with multiple antennas and an IRS with a large number of reflecting elements are deployed to serve multiple users. An energy-efficient design is developed to maximise the energy efficiency of the system by considering both transmit power and IRS phase shift constraints. Specifically, we propose a covariance matrix adaptation evolution strategy (CMA-ES) and Dinkelbach's method based energy efficiency optimisation algorithm to tackle the joint optimisation problem of IRS phase shift and precoding matrix while satisfying the maximum transmit power constraint. Simulation results show that our proposed algorithm outperforms three baseline algorithms in the literature, including random selection (RS) method, local search (LS) method and cross-entropy (CE) method, in terms of much higher energy efficiency under different parameter settings.

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454