Energy-Latency Aware Intelligent Reflecting Surface Aided Multi-cell Mobile Edge Computing

作     者：Xu, Wenhan Yu, Jiadong Wu, Yuan Tsang, Danny H.K. 

作者机构：The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong  Guangdong Guangzhou511400 China The State Key Lab of Internet of Things for Smart City China The Department of Computer and Information Science The University of Macau China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Mobile edge computing 

摘      要：The explosive development of the Internet of Things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighboring cells and cooperate to handle resource exhaustion. We aim to minimize the joint energy and latency cost, by jointly optimizing computation tasks, edge computing resources, user beamforming, and IRS phase shifts. The problem is decomposed into two subproblems—the MEC subproblem and the IRS communication subproblem—using the block coordinate descent (BCD) technique. The MEC subproblem is reformulated as a nonconvex quadratic constrained problem (QCP), while the IRS communication subproblem is transformed into a weight-sum-rate problem with auxiliary variables. We propose an efficient algorithm to iteratively optimize MEC resources and IRS communication until convergence. Numerical results show that our algorithm outperforms benchmarks and that multi-cell MEC systems achieve additional performance gains when supported by IRS. Copyright © 2023, The Authors. All rights reserved.