Zhao Ke, Song Zhiqun, Li Yong, Li Xingjian, Liu Lizhe, Wang Bin
54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.
National Key Laboratory of Advanced Communication Networks, Shijiazhuang 050081, China.
Entropy (Basel). 2024 Dec 5;26(12):1056. doi: 10.3390/e26121056.
This paper investigates the design of active and passive beamforming in a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output (MU-MISO) system with the objective of maximizing the sum rate. We propose a deep evolution policy (DEP)-based algorithm to derive the optimal beamforming strategy by generating multiple agents, each utilizing distinct deep neural networks (DNNs). Additionally, a random subspace selection (RSS) strategy is incorporated to effectively balance exploitation and exploration. The proposed DEP-based algorithm operates without the need for alternating iterations, gradient descent, or backpropagation, enabling simultaneous optimization of both active and passive beamforming. Simulation results indicate that the proposed algorithm can bring significant performance enhancements.
本文研究了可重构智能表面（RIS）辅助的多用户多输入单输出（MU-MISO）系统中的有源和无源波束成形设计，目标是最大化和速率。我们提出了一种基于深度进化策略（DEP）的算法，通过生成多个智能体来推导最优波束成形策略，每个智能体使用不同的深度神经网络（DNN）。此外，还引入了一种随机子空间选择（RSS）策略，以有效地平衡利用和探索。所提出的基于DEP的算法无需交替迭代、梯度下降或反向传播即可运行，能够同时优化有源和无源波束成形。仿真结果表明，所提出的算法可以带来显著的性能提升。
