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.

翻译：物联网（IoT）的爆炸式发展推动了对移动边缘计算（MEC）的广泛关注，后者在网络边缘提供计算资源以满足计算密集型和延迟敏感型应用的需求。智能反射面（IRS）作为解决MEC系统中卸载上行链路传输阻塞问题的一种方案而受到关注。本文研究IRS辅助的多小区网络，使服务器能够服务相邻小区并协作应对资源耗尽问题。我们旨在通过联合优化计算任务、边缘计算资源、用户波束成形以及IRS相位偏移，最小化联合能量与延迟成本。利用块坐标下降（BCD）技术将问题分解为两个子问题——MEC子问题和IRS通信子问题。MEC子问题被重构为非凸二次约束问题（QCP），而IRS通信子问题则转化为带辅助变量的权重和速率问题。我们提出了一种高效算法，通过迭代优化MEC资源和IRS通信直至收敛。数值结果表明，所提算法优于基准方法，并且多小区MEC系统在IRS支持下可获得额外性能增益。