基于交叉注意力机制的频谱传感在一般化的高斯噪声中
Haolei Xi1, Wei Guo2,3, Yanqing Yang4
1Xinjiang University, School of Computer Science and Technology, Urumqi, 830046, China.
Scientific reports
|October 6, 2024
概括
本研究引入了一种新的时频交叉融合网络 (TFCFN),用于改进认知无线电网络的频谱传感,在各种噪声条件下优于现有方法.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 对认知无线电 (CR) 网络来说,频谱传感 (SS) 对于有效利用置频谱至关重要.
- 由于复杂的通道噪声特征,特别是非高斯噪声,精确的SS具有挑战性.
- 现有的SS方法通常依赖于单一的特征,限制在不同噪声条件下的性能.
研究的目的:
- 提出一个新的时频交叉融合网络 (TFCFN),以提高频谱传感性能.
- 通过克服单一特征限制,在非高斯噪声环境中提高SS准确性.
- 为可靠的信号分类开发一个强大的特征提取和融合机制.
主要方法:
- 用于捕捉原始信号中的长期时间依赖性.
- 使用快速里埃转换 (FFT) 来提取频域信息.
- 应用卷积神经网络 (CNN) 用于频率领域的局部空间特征提取.
- 集成的时域和频域特征使用交叉注意力机制进行动态融合.
主要成果:
- 与基线方法相比,拟议的TFCFN在高斯噪声和非高斯噪声方面表现出更高的检测能力.
- 在不同的噪音环境中,TFCFN保持了较低的计算复杂性.
- 在GGD噪音下 (形状参数0.5) 实现了10%的错误报警概率,超过90%的检测概率在-16dB SNR下 (形状参数0.5).
结论:
- TFCFN有效地融合了时间域和频域特征,以便在频谱传感中进行可靠的信号分类.
- 拟议的方法显著提高SS性能,特别是在具有挑战性的非高斯噪声条件下.
- 在认知无线电网络中,TFCFN为可靠的频谱利用提供了一个有前途的解决方案.
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