在有限的通道状态信息条件下无线通信的智能反干扰决策算法
Feng Zhang1,2, Yingtao Niu3, Quan Zhou4
1School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Scientific reports
|February 20, 2025
概括
本研究介绍了一种智能反干扰算法,用于有限的通道状态信息 (CSI) 的无线通信. 这种新的方法提高了融合速度和对阻塞的性能,优于现有的深度强化学习方法.
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:
- 深度强化学习 (DRL) 对抗干扰是有效的,但通常需要完整的通道状态信息 (CSI).
- 对于基于DRL的反干扰系统来说,有限的CSI构成了重大挑战.
- 在DRL算法中优化勘探速度衰变因子是复杂的.
研究的目的:
- 为在有限的CSI条件下运行的无线系统开发智能反干扰决策算法.
- 为探索速度衰变因子提出一个自动调整算法.
- 设计一个针对这种情况的深度循环Q网络 (DRQN) 架构.
主要方法:
- 以部分可观测的马尔科夫决策过程 (POMDP) 为抗干扰问题的建模.
- 开发一个 DRQN 架构,结合长期短期记忆 (LSTM) 网络来进行时间特征学习.
- 实现一个自动调整算法,用于勘探速度衰变因子.
主要成果:
- 拟议的算法通过自动调整的勘探速度衰变因子实现了近乎最佳的性能.
- 收时间显著缩短:在周期性和智能干扰下分别比双DQN (DDQN) 快45%和32%.
- 与深度Q网络 (DQN) 和Q学习 (QL) 相比,改善了规范化吞吐量和优越的融合性能.
结论:
- 基于DRQN的新型智能反干扰算法有效地解决了有限CSI的挑战.
- 自动调整勘探速度衰变因子可提高决策效率和性能.
- 这种方法为无线通信中的防干扰提供了强大的解决方案,优于传统的DRL方法.
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