Deep Reinforcement Learning Based Uplink Configuration in Multi-RIS Assisted MEC Network

Multi-access edge computing (MEC) is an important enabler of computationally demanding and delay sensitive intelligent applications, placing significant strain on traditional cloud computing systems. As massive multiple access and high offloading data rates are envisioned for the 6th generation (6G) networks, new paradigms that can support next generation multiple access systems have appeared, e.g., the reconfigurable intelligent surfaces (RIS). Particularly, a time-division multiple access (TDMA) MEC system can leverage the integration of multiple RISs. By regulating the interplay between multiple access control and RIS phase configuration, the inclusion of multiple RISs can improve the signal quality providing better channel conditions for uplink (UL) offloading. To that end, aiming to address the offloading sum-rate maximization problem in a multi-RIS assisted TDMA MEC network, we employ deep reinforcement learning (DRL) and propose a deep deterministic policy gradient (DDPG) based algorithm, i.e., UL-RIS-DDPG. DDPG is a DRL variant designed for high-dimensional continuous action and state spaces induced by the considered problem. The UL-RIS-DDPG algorithm jointly optimizes key parameters, i.e., the users' transmission powers, the RISs phase shift design, the user-RIS association, and the UL transmission scheduling and improves significantly the network's offloading sum-rate. The simulation results demonstrate that the algorithm adapts to the network environment offering flexible RIS selection and efficient time slot allocation, outperforming the benchmarks.