智能反干扰通信技术与电磁频谱功能认知
Hui Zhao1, Guobin Zhao1, Xichun Wang2
1College of Aviation Foundation, Aviation University Air Force, Jilin, China.
PloS one
|April 24, 2025
概括
本研究介绍了一种使用深度神经网络和游戏理论进行强大的无线通信的智能反干扰算法. 这种新的方法显著改善了在动态电磁环境中的信号干扰识别和抑制.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 无线通信中的复杂信号干扰降低了传输质量和可靠性.
- 传统的反干扰方法与动态的电磁环境和计算需求作斗争.
- 无线技术的快速发展需要适应性干扰管理解决方案.
研究的目的:
- 开发一种智能反干扰算法,以提高无线通信的可靠性.
- 解决现有的静态和计算密集型干扰抑制技术的局限性.
- 在动态电磁频谱环境中提高适应性和降低错误率.
主要方法:
- 结合了深度神经网络 (DNN) 用于特征提取和游戏理论用于战略优化.
- 采用近期战略优化模型进行动态通信调整.
- 使用DNN来识别信号特征,产生匹配的干扰,并实现抑制.
主要成果:
- 拟议的算法在识别干扰信号方面实现了95.23%的准确性.
- 显示了85.47%的反干扰准确率,优于现有模型.
- 与随机森林和深度Q网络模型相比,性能显著提高.
结论:
- 智能算法有效地抑制了动态环境中的复杂信号干扰.
- 为防干扰通信技术开发提供了新且适应性的解决方案.
- 突出结合DNN和游戏理论在未来无线系统中的潜力.
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