机器学习辅助循环静止光谱分析用于联合信号分类和干扰器检测在认知无线电的物理层
Tassadaq Nawaz1, Ali Alzahrani1
1Department of Computer Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了用于认知无线电频谱表征的强大算法,提高了干扰检测和分类准确度,以确保安全通信. 这种新的方法有效地识别了宽带环境中的合法信号和隐形干扰攻击.
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:
- 频谱稀缺需要动态的频谱接入解决方案.
- 认知无线电 (CR) 提供智能频谱管理功能.
- CRs的适应性适合先进的干扰和防干扰系统设计.
研究的目的:
- 介绍一种新的,强大的算法,用于宽带认知无线电终端的频谱表征.
- 准确地将窄带信号分类为合法的或偷偷地被干扰的.
主要方法:
- 利用循环静止特征检测来测量光谱相关密度.
- 提取循环和角度频率配置文件作为特征集.
- 使用人工神经网络进行信号分类.
主要成果:
- 与现有方法相比,该算法显示出更高的分类准确性.
- 在宽带频谱传感中有效表征窄带信号.
- 成功识别了多色调和调制的隐形干扰攻击.
结论:
- 拟议的算法为认知无线电系统中的频谱表征提供了一个强大的解决方案.
- 高分类准确性支持先进的干扰和反干扰应用.
- 潜在的应用范围包括商业和军事通信系统.
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