基于强化学习的RIS辅助空地通信系统优化研究
Yuanyuan Yao1,2, Xinyang Liu1,2, Sai Huang3
1Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究介绍了使用无人机 (UAV) 和可重新配置的智能表面 (RIS) 的空地网络,以改善城市紧急通讯. 拟议的D3QN-WF算法显著提高了网络总和速率和吞吐量.
科学领域:
- 无线通信无线通信
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 由于建筑物障碍,城市环境对基站 (BS) 通信网络构成挑战.
- 无人驾驶飞行器 (UAV) 为增强空中通信网络提供了灵活的解决方案.
- 可重新配置的智能表面 (RIS) 可以动态调整无线信号传播.
研究的目的:
- 提出使用无人机和RIS的空地无线网络,以克服城市通信障碍.
- 提高无人机支持的多输入单输出 (MISO) 网络在具有挑战性的城市环境中的性能.
- 开发用于优化无人机移动,RIS相位转移和用户功率分配的先进算法.
主要方法:
- 无人机被模拟为一个能够进行3D运动和通道传感的智能代理.
- 使用零强制 (ZF) 预编码来减轻地面用户的干扰.
- 两种深度强化学习 (DRL) 算法,D3QN-WF和DDQN-WF,被建议用于联合设计优化.
主要成果:
- 与DDQN-WF基线相比,D3QN-WF算法显示总和率增加了15.9%.
- 使用D3QN-WF算法实现了50.1%的更高吞吐量.
- 在模拟中,D3QN-WF算法表现出明显更快的收率.
结论:
- 拟议的空地网络与无人机和RIS有效地提高了城市紧急通信的弹性.
- 基于DRL的优化,特别是D3QN-WF,为管理复杂的无线环境提供了优越的方法.
- 该系统有望在城市阻塞地区提高移动网络性能.
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