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

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.

Keywords

• mine communication
• underground signal recognition
• nakagami-m fading channel
• higher-order cumulant
• deep neural network
• dnn model