Maximizing Computation Efficiency and Fairness of Multi-UAV Assisted MEC System Supported by RIS

Recently, unmanned aerial vehicles (UAVs) have been widely used in mobile edge computing (MEC) systems to compute part of the computing tasks offloaded from ground equipments (GEs) due to their high mobility and flexibility. In addition, reconfigurable intelligent surfaces (RIS), as an emerging technology, can enhance the wireless propagation environment in wireless networks and improve the computation efficiency of the system. In this paper, we propose a multi-UAV-assisted MEC system supported by RIS, where GEs can partially offload tasks to UAVs for computation. A joint optimization problem is formulated to maximize the weighted computation efficiency and fairness by optimizing the user association state, offloading ratio, computing resource allocation, the trajectory of UAVs and the actual RIS phase shift design. To solve the problem, a Fuzzy C-Means-Multi-Agent Deep Deterministic Policy Gradient Alternating Iterative (FMAI) algorithm is designed. In this algorithm, we firstly design a GE-UAV Association and Variable Initialization Combine Fuzzy C-Means Clustering (GUAIFCM) algorithm to solve GE-UAV association strategy. Then we introduce the multi-agent deep deterministic policy gradient alternating iteration (MADDPGAI) algorithm to solve the computation resource allocation, the trajectory of UAVs, task allocation and RIS phase shift. The simulation results show that the proposed scheme can significantly improve the computation efficiency and fairness of RIS-assisted multi-UAV MEC system compared with the benchmark scheme.