通过参数化共享机制提升多智能体强化学习，优化多功能RIS辅助的非正交多址网络能效。
Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks
- 设计参数化共享多智能体混合强化学习框架，分别用PPO和DQN处理连续与离散变量
- 仿真显示所提方案在多种场景下能效均最优，最高达基准方案的1.35倍
- 适合研究智能反射面、非正交多址及能源感知通信系统的工程师与学者
多功能可重构智能表面（MF-RIS）因其主动信号增强能力和能量采集自供能特性，可提升通信效率。本文研究多MF-RIS辅助非正交多址（NOMA）下行链路网络架构，通过联合优化功率分配、波束成形、MF-RIS幅度/相位配置、能量采集比例及位置，最大化系统能效（EE），满足功率预算、用户速率需求及自供能约束。提出参数化共享多智能体混合强化学习（PMHRL）方法，其中多智能体近端策略优化（PPO）和深度Q网络（DQN）分别处理连续与离散决策变量。仿真表明，相较无参数共享、纯PPO/DQN等基线，所提方案能效最高；且多MF-RIS-NOMA系统在不同多址场景下均优于无能量采集/放大、传统RIS及无部署的基准方案。
原文摘要 · Abstract (English)
Multi-functional reconfigurable intelligent surface (MF-RIS) is conceived to address the communication efficiency thanks to its extended signal coverage from its active RIS capability and self-sustainability from energy harvesting (EH). We investigate the architecture of multi-MF-RISs to assist non-orthogonal multiple access (NOMA) downlink networks. We formulate an energy efficiency (EE) maximization problem by optimizing power allocation, transmit beamforming and MF-RIS configurations of amplitudes, phase-shifts and EH ratios, as well as the position of MF-RISs, while satisfying constraints of available power, user rate requirements, and self-sustainability property. We design a parametrized sharing scheme for multi-agent hybrid deep reinforcement learning (PMHRL), where the multi-agent proximal policy optimization (PPO) and deep-Q network (DQN) handle continuous and discrete variables, respectively. The simulation results have demonstrated that proposed PMHRL has the highest EE compared to other benchmarks, including cases without parametrized sharing, pure PPO and DQN. Moreover, the proposed multi-MF-RISs-aided downlink NOMA achieves the highest EE compared to scenarios of no-EH/amplification, traditional RISs, and deployment without RISs/MF-RISs under different multiple access.
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