用可重构智能表面提升低轨网络能效，结合联邦强化学习优化多功能设备配置。
Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks
- 采用联邦增强的多智能体强化学习，协同优化低轨卫星中多功能表面的反射、折射与能量采集参数。
- 相比集中式和分布式强化学习，能效提升显著，在测试场景中最高提升达42%。
- 适合关注低轨通信系统能效优化与智能表面应用的研究者或工程团队。
本文提出一种在低地球轨道（LEO）部署多功能可重构智能表面（MF-RIS）的新网络架构。与仅支持信号反射的传统RIS不同，MF-RIS具备反射、折射、信号放大及从无线信号中采集能量的能力。针对阴影区域太阳能不可用导致的高能耗问题，通过在LEO部署MF-RIS以增强信号覆盖并提升能量效率（EE）。为此，我们建立了长期能效优化模型，优化参数包括放大系数、相位偏移、能量采集比例及LEO发射波束成形。为应对非凸、非线性难题，设计了联邦学习增强的多智能体深度确定性策略梯度（FEMAD）算法：各智能体通过交互学习最优动作策略，联邦学习则实现智能体间隐式信息共享。数值结果表明，所提方案在能效上显著优于中心化深度强化学习及分布式多智能体DDPG基准。此外，该架构在多种对比场景中表现最优，包括固定/无能量采集的MF-RIS、传统仅反射RIS以及无RIS部署的情况。
原文摘要 · Abstract (English)
In this paper, a novel network architecture that deploys the multi-functional reconfigurable intelligent surface (MF-RIS) 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 in 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 for MF-RIS configurations, including amplification and 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 DDPG of each agent can provide the optimal action policy from its interaction to 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 deep deterministic policy gradient (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.
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