D-GF-CNN Algorithm for Modulation Recognition

This paper presents a novel modulation recognition algorithm based on dilated convolutional neural network with a new defined GF regularization function, named as D-GF-CNN algorithm. Firstly, an asynchronous delay sampling (ADS) technique is introduced. Via the defined ADS, the received signal is converted into an asynchronous delay histogram (ADH). The ADH of different modulation signals has distinct characteristics, which provides great convenience for the neural network to identify the modulation mode. Then, the pixel point matrix of the ADH is convolved with the dilated convolution kernel of the convolutional neural network, and the automatic extraction of signal features is completed so that the manual feature extraction processing can be effectively avoided. Finally, a novel GF regularization function is given, which can improve the constraint ability of the loss function on the weight and effectively weaken the influence of network over-fitting on the modulation recognition accuracy. Theoretical analysis and simulation experiments show that the proposed algorithm provides several advantages, for example: (1) automatically extract features; (2) effectively prevent network over-fitting; (3) significantly improve recognition accuracy in the lower SNR scenarios.