在认知无线电网络中基于STFT-RADN的频谱传感方法
Anyi Wang1, Tao Zhu1, Qifeng Meng1
1School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究介绍了一种新的频谱传感算法,用于使用短时间里埃变换和剩余注意力密度网络的认知无线电网络. 该方法增强了特征提取,在低信号条件下提高了性能.
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:
- 传统的基于CNN的频谱传感与特征表示和提取斗争.
- 浅层网络限制了认知无线电网络中现有的算法的能力.
- 在低信号噪声比 (SNR) 条件下需要强大的频谱传感.
研究的目的:
- 提出一种频谱传感算法,克服传统CNN的局限性.
- 增强功能提取和利用,以提高认知无线电网络性能.
- 开发一种强大的方法,适应各种调制方案.
主要方法:
- 使用短时间里埃转换 (STFT) 将信号转换为时间频谱图.
- 使用剩余注意密度网络 (RADN) 与剩余密度区块 (RID) 和CBAM进行深度特征提取.
- 通过使用时间频率图像来训练RADN作为频谱传感分类器.
主要成果:
- STFT-RADN方法显著提高了频谱传感性能,特别是在低SNR时.
- 在不同的调制方案中展示了高的检测概率和强大的稳定性.
- 性能优于传统的基于深度学习的频谱传感方法.
结论:
- 拟议的STFT-RADN算法为认知无线电网络中的频谱传感提供了一个强大的解决方案.
- 这种方法有效地解决了特征表示和提取挑战.
- 该方法提供了增强的性能和稳定性,特别是在具有挑战性的低SNR环境中.
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