DRL-Based Resource Allocation of RIS-Aided OFDMA System with Limited Fronthaul Capacity

In this paper, resource allocation and RIS regulation in reconfigurable intelligent surface (RIS) enhanced orthogonal frequency division multiple access (OFDMA) systems in cloud radio access networks (C-RAN) are investigated. Specifically, we consider the uplink, where the remote radio head (RRH) is compressed by independent quantization of the received signal and has the quantized bits transmitted to the baseband unit (BBU) over a limited capacity fronthaul link. To maximize the total rate of the system, we propose a multi-agent deep reinforcement learning (MADRL) approach. We use the multi-agent double deep Q network (MADDQN) algorithm to optimize RRH selection and subcarrier allocation, and the multi-agent depth deterministic strategy gradient (MADDPG) algorithm to optimize power allocation and RIS reflection coefficient regulation. Simulation results show that the proposed method can efficiently maximize the sum rate of RIS-aided OFDMA uplink while satisfying the constraint of limited fronthaul capacity in C-RAN.