一种基于政策的深度进化方法,用于RIS增强通信系统
Ke Zhao1,2, Zhiqun Song1,2, Yong Li1,2
154th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种新的深度进化政策 (DEP) 算法,用于优化可重新配置智能表面 (RIS) 辅助系统中的光束成形. 在多用户系统中,DEP算法提高了总和率,而无需复杂的代或梯度计算.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 电信领域的人工智能
背景情况:
- 可重新配置的智能表面 (RIS) 提供了对无线环境的增强控制.
- 多用户多输入单输出 (MU-MISO) 系统对于高效的无线数据传输至关重要.
- 优化光束成形是最大化MU-MISO网络系统性能的关键.
研究的目的:
- 在RIS辅助的MU-MISO系统中设计和研究主动和被动光束成型策略.
- 为了最大限度地提高通信系统的总和率.
- 开发一种高效的算法,同时优化主动和被动光束成形.
主要方法:
- 一个基于深度进化政策 (DEP) 的算法被提出.
- 采用了多个代理,每个代理使用不同的深度神经网络 (DNN).
- 一个随机子空间选择 (RSS) 策略被整合到勘探-开发平衡中.
- 该算法避免了交替代,梯度下降和反向传播.
主要成果:
- 提出的基于DEP的算法实现了同时优化主动和被动光束成形.
- 在模拟结果中观察到显著的性能提升.
- 该算法在不需要传统的代优化技术的情况下证明了效率.
结论:
- 基于DEP的算法为RIS辅助MU-MISO系统中的束形优化提供了有效的方法.
- 这种方法为传统的优化技术提供了有希望的替代方案,提高了系统性能.
- 该研究强调了人工智能驱动的解决方案对先进的无线通信系统的潜力.
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