Automatic Modulation Classification for OFDM Signals Based on CNN With α-Softmax Loss Function

Automatic modulation classification (AMC) plays an important role in cooperative and noncooperative contexts. Many studies on the application of deep learning (DL) to AMC have widely been reported. This article deals with an AMC for orthogonal frequency division multiplexing signals based on convolutional neural network (CNN) among DL methods. For AMC, we propose a loss function, which we refer to as alpha-softmax loss function and present a deep CNN model utilizing the proposed loss function. By optimizing the proposed loss function, we can further separate the features of one modulation scheme from those of the other modulation schemes for the classification performance improvement. Through computer simulations, we show that the proposed model with $\alpha$-softmax loss function outperforms the conventional ones in terms of classification accuracy.