分布式多代理深度增强基于学习的传输功率控制在蜂网络
1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种新的多代理深度强化学习 (MADRL) 方法,用于蜂网络传输功率控制的长期短期记忆 (LSTM). 该方法通过使基站能够使用本地数据有效地管理干扰来提高总比率的性能.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 干扰管理对于多细胞网络的性能至关重要.
- 控制基站下链传输功率对于高数据速率至关重要,但随着细胞密度的增加而变得复杂.
研究的目的:
- 提出使用多代理深度增强学习 (MADRL) 的传输功率控制方案,以最大限度地提高多细胞网络中的总比率.
- 通过整合长期短期记忆 (LSTM) 架构来增强MADRL,以改善状态保留和决策.
主要方法:
- 开发了一种多代理深度增强学习 (MADRL) 框架,用于下链传输功率控制.
- 长期短期内存 (LSTM) 架构被纳入MADRL代理,以利用历史状态信息.
- 每个基站代理仅使用本地通道状态信息,最大限度地减少代理之间的通信.
主要成果:
- 拟议的MADRL-LSTM方案显示,与现有的MADRL方法相比,总比率性能得到了改进.
- 该方案有效地减少了基站链路之间交换的信号信息数量.
- 当地通道状态信息的利用对于去中心化控制来说是有效的.
结论:
- 在密集的蜂网络中,LSTM与MADRL的集成为传输功率控制提供了强大的解决方案.
- 使用本地信息进行分散控制是可行的和高效的,减少了通讯开销.
- 拟议的方法通过有效的干扰管理来提高整体网络容量和用户数据速率.
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01:23Reinforcement
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01:30Control of Power Flow
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01:23Maximum Power Flow and Line Loadability
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