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Joint Beamforming With Extremely Large Scale RIS: A Sequential Multi-Agent A2C Approach

delete2026-01-30
delete0
PRE
AI
C
Chai Zhi
J
Jiajie Xu
J
Justin P. Coon
M
Mohamed‐Slim Alouini
DOI：10.1109/LWC.2026.3660067delete
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摘要

摘要

En 中文
在RIS辅助的多用户多输入单输出（MU-MISO）系统中，联合优化基站（BS）预编码矩阵和可重构智能表面（RIS）相位具有挑战性，尤其是在RIS规模极大的情况下。本文提出了一种基于顺序多智能体优势演员-评论家（A2C）框架的深度强化学习（DRL）方法，该方法考虑了离散RIS相位、不完善的信道状态信息（CSI）以及用户间信道相关性。分析了计算复杂度，并将所提出的方法与零强制（ZF）、最小均方误差（MMSE）以及单智能体A2C波束成形器在总频谱效率（SE）方面进行了比较。仿真结果表明，所提出的算法能够实现更高的SE，并对适度的信道估计误差具有一定的鲁棒性。
Keyword:
RIS
joint beamforming
multi-agent deep reinforcement learning

期刊

I
IEEE Wireless Communications Letters
IF:
5.5
论文数:
824
被引数:
0

机构

K
king abdullah university of science and technology
学者数:
1.7K
论文数: 630
被引数: 0
U
university of oxford
学者数:
9.8W
论文数: 8.6W
被引数: 137
引用论文

引用论文

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