Automatic Modulation Recognition Through Wireless Sensor Networks in Aeronautical Wireless Channel

The Doppler effect causes frequency offset and spectrum spread of communication signals, which increases the difficulty of feature extraction when identifying the modulation mode in aeronautical wireless channels. To improve the signal modulation recognition capability of the single monitoring node in Wireless Sensor Networks (WSN), a method based on the bidirectional convolution gated recurrent deep neural network (Bi-CGDNN) with an attention mechanism is proposed in this paper. We design the framework with different types of network layers, realizing frequency offset suppression and feature mapping. In addition, quadrature sampling signals and instantaneous feature statistics are collected to generate the dataset. Thus, the waveform features and phase-frequency information of the signals can be used for combinatorial analysis. In particular, the attention mechanism is introduced to optimize the framework for better feature perception and higher recognition rates. Experimental results show that our method achieves great recognition effects at different maximum Doppler frequencies. The recognition rate reaches 98.1% at the signal-to-noise ratio (SNRs) of 8dB with the Doppler frequency of 200Hz.