In mobile edge computing (MEC) systems, the wireless channel condition is a critical factor affecting both the communication power consumption and computation rate of the offloading tasks. This paper exploits the idea of cooperative transmission and employing reconfigurable intelligent surface (RIS) in MEC to improve the channel condition and maximize computation efficiency (CE). The resulting problem couples various wireless resources in both uplink and downlink, which calls for the joint design of the user association, receive/downlink beamforming vectors, transmit power of users, task partition strategies for local computing and offloading, and uplink/downlink phase shifts at the RIS. To tackle the challenges brought by the combinatorial optimization problem, the group sparsity structure of the beamforming vectors determined by user association is exploited. Furthermore, while the CE does not explicitly depend on the downlink phase shifts, instead of simply finding a feasible solution, we exploit the hidden relationship between them and convert this relationship into an explicit form for optimization. Then the resulting problem is solved via the alternating maximization framework, and the nonconvexity of each subproblem is handled individually. Simulation results show that cooperative transmission and RIS deployment can significantly improve the CE and demonstrate the importance of optimizing the downlink phase shifts with an explicit form.

翻译：在移动边缘计算系统中，无线信道条件是影响通信能耗和卸载任务计算速率的关键因素。本文利用协同传输思想，在MEC中引入可重构智能表面以改善信道条件并最大化计算效率。由此产生的问题耦合了上下行多种无线资源，需要联合设计用户关联、接收/下行波束成形向量、用户发射功率、本地计算与任务卸载的划分策略，以及RIS处的上下行相移。为应对组合优化带来的挑战，本文利用用户关联决定的波束成形向量的组稀疏结构。此外，尽管计算效率并不显式依赖于下行相移，本文不仅寻找可行解，而是挖掘两者间的隐含关系并将其转化为显式形式进行优化。随后通过交替最大化框架求解该问题，并分别处理每个子问题的非凸性。仿真结果表明，协同传输与RIS部署能够显著提升计算效率，并证明了采用显式形式优化下行相移的重要性。