对非圆形信号的频谱传感使用增强的共差-矩阵-意识深度卷积神经网络.
Songlin Chen1, Zhenqing He2,3, Wenze Song2
1Southwest China Institute of Electronic Technology, Chengdu 610036, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了一种深度学习方法,用于认知无线电网络中的频谱传感. 这种新的方法通过使用带有卷积神经网络 (CNN) 的增强共变矩阵来增强非圆形信号的检测.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 认知无线电网络可以实现动态频谱访问.
- 频谱传感对于识别可用的频段至关重要.
- 检测非圆形信号在频谱传感方面带来了独特的挑战.
研究的目的:
- 为认知无线电网络开发先进的频谱传感技术.
- 来自主要用户的非圆形信号的检测精度提高.
- 为了利用深度学习来增强光谱占用检测.
主要方法:
- 一种基于深度学习的频谱传感方法,使用卷积神经网络 (CNN).
- 使用增强样本协差矩阵,结合标准和补充协差矩阵.
- 训练CNN使用增强共变矩阵来学习信号模式.
主要成果:
- 拟议的方法显著提高了频谱传感性能.
- 证明了对非圆形信号的增强检测精度.
- 与现有方法相比,展示了强大的概括能力.
结论:
- 增强的共变矩阵感知CNN有效地利用非循环信号属性.
- 这种方法在没有严格的模型假设的情况下提供了更好的性能.
- 这种深度学习技术为认知无线电中的频谱传感提供了强大的解决方案.
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