Research Article
Jointly power allocation and phase shift optimization for RIS empowered downlink cellular networks
@ARTICLE{10.4108/eetinis.v10i4.4359, author={Phuc Quang Truong and Tan Do-Duy and Van-Ca Phan and Antonino Masaracchia}, title={Jointly power allocation and phase shift optimization for RIS empowered downlink cellular networks}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={10}, number={4}, publisher={EAI}, journal_a={INIS}, year={2023}, month={12}, keywords={6G, Optimization, Phase-Shaft Optimization, QoS, Resource Allocation, RIS, Throughput Maximization}, doi={10.4108/eetinis.v10i4.4359} }
- Phuc Quang Truong
Tan Do-Duy
Van-Ca Phan
Antonino Masaracchia
Year: 2023
Jointly power allocation and phase shift optimization for RIS empowered downlink cellular networks
INIS
EAI
DOI: 10.4108/eetinis.v10i4.4359
Abstract
Reconfigurable Intelligent Surfaces (RIS) have been highlighted by the research community as a key enabling technology for the enhancement of next-generation wireless network performance, including energy efficiency, spectral efficiency, and network throughput. This paper investigates how RIS-assisted communication can effectively maximize the downlink throughput of a cellular network. Specifically, the paper considers a communication scenario where a single base station serves multiple ground users with the aid of an RIS placed on a building facade. For such a communication scenario, we considered an optimization problem aimed at maximizing the overall downlink throughput by jointly optimizing power allocation at the base station and phase shift of RIS reflecting elements, subject to power consumption and quality-of-service constraints. To address its non-convex nature, the original optimization problem has been divided into two subproblems. The first one, for power control with fixed phase shift values, is a convex problem that can be easily solved. Subsequently, a phase shift searching procedure to solve the non-convex problem of RIS phase shift optimization has been adopted. The results from numerical simulations show that the proposed method outperforms other conventional methods proposed in the literature. In addition, computational complexity analysis has been conducted to prove the low complexity of the proposed method.
Copyright © 2023 P. Q. Truong et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.