Energy-Efficient Computation Offloading in Multi-RIS-Aided Cell-Free Networks

This paper investigates the computation offloading problem in a distributed reconfigurable intelligent surface (RIS)-aided cell-free network, where users offload computationally intensive tasks to their associated access points (APs) with the assistance of multiple RISs. To minimize the long-term energy cost under latency constraints, we formulate a non-convex optimization problem aimed at jointly optimizing the offloading strategy, user transmit power, RIS reflection matrix, AP beamforming, and CPU frequencies at both users and the edge server. Given the problem's complexity and the interdependence of variables, we propose a two-stage decentralized proximal policy optimization (DPPO) algorithm to effectively manage the high-dimensional decision space. Simulation results demonstrate that our algorithm reduces the long-term energy cost by approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$17\%$</tex-math></inline-formula> compared to existing methods, while ensuring that tasks are completed within the latency constraints.