Enhancing the generalization ability of deep learning model for radio signal modulation recognition

Automatic modulation recognition is a major project in the field of radio cognition; however, the generalization ability of conventional models cannot satisfy practical applications. In order to improve the generalization performance of the deep learning model and increase its recognition efficiency, we propose a novel model: ElsNet (elastic convolutional neural network). This network designs a channel optimization module, by inputting the average pooling information of the feature map and the intrinsic parameters of the batch normalization layer, to dynamically optimize the connection relations between network neurons and enhance the generalization ability of the model. ElsNet achieves an accuracy of about 94% at signal-to-noise ratios of 0-20 dB. Subsequent experiments have also demonstrated that, the ElsNet has a satisfying performance in transferred data sets and a peak accuracy of 82% through transfer learning, which to a certain extent alleviates the problem that the current signal modulation recognition can only be applied to signals with the same modulation parameters as the training dataset and has poor performance in recognizing real signals with different modulation parameters.