Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks

Li-Hsiang Shen, Jyun-Jhe Huang, Kai-Ten Feng, Lie-Liang Yang, Jen-Ming Wu

Abstract: In this paper, a novel network architecture that deploys the multi-functional reconfigurable intelligent surface (MFRIS) in low-Earth orbit (LEO) is proposed. Unlike traditional RIS with only signal reflection capability, the MF-RIS can reflect, refract, and amplify signals, as well as harvest energy from wireless signals. Given the high energy demands in shadow regions where solar energy is unavailable, MF-RIS is deployed on LEO to enhance signal coverage and improve energy efficiency (EE). To address this, we formulate a long-term EE optimization problem by determining the optimal parameters of MF-RIS, including amplification, phase-shifts, energy harvesting ratios, and LEO transmit beamforming. To address the complex non-convex and non-linear problem, a federated learning enhanced multi-agent deep deterministic policy gradient (FEMAD) scheme is designed. Multi-agent deep deterministic policy gradient (DDPG) of each agent can provide the optimal action policy from its interaction with environments, whereas federated learning enables the hidden information exchange among multi-agents. In numerical results, we can observe significant EE improvements compared to the other benchmarks, including centralized deep reinforcement learning as well as distributed multi-agent DDPG. Additionally, the proposed LEO-MF-RIS architecture has demonstrated its effectiveness, achieving the highest EE performance compared to the scenarios of fixed/no energy harvesting in MF-RIS, traditional reflection-only RIS, and deployment without RISs/MF-RISs.

External IDs:dblp:conf/icc/ShenHFYW25