多域融合深度学习用于空间认知无线电中的自动调制识别
Shunhu Hou1, Yaoyao Dong2, Yuhai Li1
1Graduate School, Space Engineering University, Beijing, 101416, China.
Scientific reports
|July 3, 2023
概括
这项研究引入了一种新的联合深度学习模型,用于空间认知无线电 (SCR) 中的自动调制识别 (AMR). 综合方法显著提高了信号分类的准确性,优于单一网络模型.
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:
- 自动调制识别 (AMR) 对空间认知无线电 (SCR) 系统至关重要.
- 深度学习模型在信号分类方面表现出色,但在复杂的无线环境中扎,这些无线环境具有多种不同的信号和干扰.
- 单个深度学习网络 (DLNs) 通常无法提取独特的特征来准确分类所有信号类型.
研究的目的:
- 提出一种新的时间频域联合识别模型,以提高AMR的准确性.
- 将两个不同的深度学习网络结合起来,以克服复杂信号环境中单个模型的局限性.
主要方法:
- 一个多通道卷积长短期深度神经网络 (MCLDNN) 在相位和方位 (IQ) 信号上进行训练,以获得更简单的调制模式.
- 采用快速里埃转换 (FFT) 的三层双向封闭反复单元 (BiGRU3) 网络,用于具有类似时间域但频率域特征不同的信号.
- FFT被用来提取频域振幅和相位 (FDAP) 信息,用于具有挑战性的信号对,如AM-DSB和WBFM.
主要成果:
- 拟议的联合模型在RML2016.10a数据集上实现了94.94%的高整体识别准确率,在RML2016.10b数据集上达到96.69%.
- 与单一网络方法相比,观察到识别准确性的显著改善.
- 对于AM-DSB和WBFM信号的具体改进分别达到17%和18.2%,证明了该模型对类似信号的有效性.
结论:
- 时间频域联合识别模型有效地提高了复杂的无线环境中的AMR准确性.
- 结合MCLDNN和BiGRU3网络,为各种信号类型提供了卓越的特征提取能力.
- 这种方法为改善空间认知无线电应用中的信号分类提供了强大的解决方案.
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