Underground signal recognition method based on higher-order cumulants and DNN model
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摘要: 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。Abstract: 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.
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