Rate optimization using low complex methods with reconfigurable intelligent surfaces

Hassouna, S., Jamshed, M. A., Ur-Rehman, M. , Imran, M. A. and Abbasi, Q. H. (2023) Rate optimization using low complex methods with reconfigurable intelligent surfaces. Journal of Information and Intelligence, 1(3), pp. 267-280. (doi: 10.1016/j.jiixd.2023.06.004)

Abstract

With the help of a developing technology called reconfigurable intelligent surfaces (RIS), it is possible to modify the propagation environment and boost the data rates of wireless communication networks. In this article, we optimized the phases of the RIS elements and performed a fair power allocation for each subcarrier over the full bandwidth in a single-input-single-output (SISO) wideband system where the user and the access point (AP) are provided with a single antenna. The data rate or its equivalent channel power is maximized by proposing different low-complex algorithms. The strongest tap maximization (STM) and power methods are compared with the semidefinite relaxation (SDR) method in terms of computational complexity and data rate performance. Runtime and complexity analysis of the suggested methods are computed and compared to reveal the actual time consumption and the required number of operations for each method. Simulation results show that with an optimized RIS, the sum rate is 2.5 times higher than with an unconfigured surface, demonstrating the RIS's tremendous advantages even in complex configurations. The data rate performance of the SDR method is higher than the power method and less than the STM method but with higher computational complexity, more than 6 million complex operations, and 50 ​min of runtime calculations compared with the other STM and power optimization methods.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Ur Rehman, Dr Masood and Jamshed, Dr Muhammad Ali and Hassouna, Saber and Abbasi, Professor Qammer
• Authors: Hassouna, S., Jamshed, M. A., Ur-Rehman, M., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Journal of Information and Intelligence
• Publisher: Elsevier
• ISSN: 2949-7159
• Published Online: 11 July 2023
• Copyright Holders: Copyright © 2023 Published by Elsevier B.V. on behalf of Xidian University
• First Published: First published in Journal of Information and Intelligence 1(3):267-280
• Publisher Policy: Reproduced under a Creative Commons license