Energy-Efficient Resource Allocation for MEC and RIS-Aided Air-Ground IoT Networks

With the blossom of Internet of Things (IoT) services and applications, the big data volumes raised by the large number of IoT devices have posed great burden on the traditional terrestrial networks. Considering the advantages of multi-access edge computing (MEC) and reconfigurable intelligent surface (RIS), this paper investigates the MEC and RIS-assisted airground IoT networks, where the joint resource allocation problem is formulated to minimize the system energy consumption. To handle the proposed nonconvex optimization problem, we decompose it into three subproblems, i.e., the coded caching placement problem, the phase shift problem and the joint multi-user association and power allocation problem. Then, we propose a deep reinforcement learning (DRL)-based Proximal Policy Optimization (PPO) algorithm to solve the joint multiuser association and power allocation problem. Moreover, the CVX technique and exhaustive search method are respectively adopted to solve the coded caching placement problem and the phase shift problem. Simulation results demonstrate that our proposed algorithm outperforms the benchmark schemes.