Electromagnetic Signal Modulation Classification Based on Multimodal Features and Reinforcement Learning

The multimodal representation of signals can give a lot of information in the field of communication modulation signal recognition. Existing approaches can use some of the modes for classification tasks, but they don't take use of the multimodal characteristics of signals well. The approach of signal modulation recognition suggested in this research is based on reinforcement learning and multimodal features. The feature set is obtained via wavelet transform using the 1D features of the signal and the constellation map features, and then the feature set is filtered using reinforcement learning to get a small subset of features to obtain the training feature set. With a small number of extra channels, the accuracy of recognition can be enhanced when utilizing the filtered feature set to categorize the modulation class of the signal. Experiments on the RML2016 dataset validate the effectiveness of our proposed method and achieve good classification performance, which is a pioneering idea for the signal modulation classification problem.