基于高阶累积量和DNN模型的井下信号识别方法

Underground signal recognition method based on higher-order cumulants and DNN model

针对矿井复杂异构的无线环境,提出一种基于高阶累积量和DNN模型的井下信号识别方法,实现了井下BPSK,QPSK,8PSK,2FSK,4FSK,8FSK,32QAM,64QAM,OFDM等数字信号的自动调制识别。分析得到9种数字信号的高阶累积量理论值,并通过傅里叶变换提高信号辨识度;分析井下小尺度衰落信道对高阶累积量的影响,推导出经过井下衰落信道后信号的高阶累积量计算表达式,根据高阶累积量理论值构造特征参数并训练DNN模型,实现信号识别。仿真分析结果表明,该方法在矿井Nakagami-m衰落信道下有出色的调制识别性能,信噪比为-5 dB时平均正确识别率为89.2%以上,信噪比为5 dB以上时平均正确识别率为100%。该方法为在特殊复杂环境下的信号识别检测提供了新思路。

In view of complex and heterogeneous wireless environment of mine,an underground signal recognition method based on higher-order cumulants and DNN model was proposed to realize automatic modulation recognition of underground digital signals of BPSK,QPSK,8PSK,2FSK,4FSK,8FSK,32QAM,64QAM,OFDM.Theoretical values of high-order cumulants of the 9 kinds of digital signals were obtained by analysis,and the signal identification was improved by Fourier transform.The influence of underground small-scale fading channels on high-order cumulants were analyzed,high-order cumulants calculation expression of the signal after passing through the underground channel was derived,and signal recognition was realized using characteristic parameters constructed according high-order cumulants to train DNN model.The simulation analysis results show that the method has excellent modulation recognition performance in mine Nakagami-m fading channel,average correct recognition rate is more than 89.2%when the signal-to-noise ratio is-5 dB,and the average correct recognition rate is 100%when the signal-to-noise ratio is 5 dB or more.The method provides a new idea for signal recognition and detection in special and complex environments.