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通过合作式多代理深度增强学习提高6G下行链接光束成形的光谱效率
Ali Al Janaby1, Hussain Al-Rizzo2, Yahya Qassim3
1Department of Communications Engineering, College of Electronics, Nineveh University, Mosul 41002, Iraq.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了一种新的多代理增强学习 (MARL) 束形算法,用于多用户多输入多输出 (MU-MIMO) 系统. 这种方法显著提高了无线通信的效率和性能,提高了信号与干扰加噪声比 (SINR).
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 多用户多输入多输出 (MU-MIMO) 系统对于现代无线网络至关重要.
- 高效的光束成型对于最大化光谱效率和用户体验至关重要.
- 现有的方法在动态环境和复杂干扰场景中面临挑战.
研究的目的:
- 为MU-MIMO系统开发和评估一种新的束形算法.
- 为了提高系统性能,利用多代理强化学习 (MARL).
- 改善信号与干扰加噪声比 (SINR) 和网络吞吐量.
主要方法:
- 采用MARL实现一个光束成形算法.
- 系统配置有两个基站,每个基站都使用统一矩形阵列 (URA) 天线.
- 每个基站的RL算法都协调,以优化SINR并最大限度地减少干扰.
主要成果:
- 拟议的MARL束形算法显示了显著的性能改进.
- 实现了网络吞吐量增加两倍以上.
- 在信号与干扰加噪声比率 (SINR) 中报告了相当大的5453%的改善.
结论:
- 基于MARL的束形算法有效地提高了MU-MIMO系统的性能.
- 光束模式的动态适应保持了高的SINR,并提高了资源利用率.
- 这种方法显示出未来高性能无线通信系统的巨大潜力.
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