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When communicating in aeronautical wireless channels, the difficulty of radio modulation recognition increases due to the loss of information caused by noise; particularly in circumstances with low signal-to-noise ratios (SNRs), it is difficult to achieve recognition rates exceeding 90.0%. To improve the radio modulation recognition performances of networks at low SNRs in complex electromagnetic environments, a modulation recognition method based on multidimensional feature analysis is proposed in this paper. It is realized through a cascaded structure including a Deep Cross Network (DCN) and an improved Visual Geometry Group Network 16 (VGG16). Our network framework is divided into two modules. In the one-dimensional data analysis module, we take the high-order cumulant of a transmitted signal as the one-dimensional feature input of the DCN. In the two-dimensional data analysis module, the color constellation density of the signal is extracted as the feature map input of the improved VGG16. Finally, we build a cascaded neural network with hybrid feature inputs for modulation recognition. Experimental results show that the recognition rate of our method is higher than 90.0% at an SNR of -4 dB. Compared with other methods, the proposed method has better recognition performance at low SNRs in aeronautical wireless channels.

A Modulation Recognition Algorithm via Hybrid Feature Analysis in Aeronautical Wireless Channel