Modulation Recognition based on Incremental Deep Learning

Incremental learning has emerged to solve the problem of incrementally updating the classification model as the number of data classes grows. There are many challenges in incremental learning such as catastrophic forgetting and learning efficiency. In this paper, we present a method of the modulation recognition based on incremental learning, that allows learning continuously with a class-incremental way. The new classes can be added into the existing model progressively from a sequential data stream. We conduct experiments on the modulation signal dataset characterized by the constellation diagram, the experimental results prove the feasibility of our incremental learning system. Our method performs similarly in classification accuracy compared to common multi-task joint training, but performs better in training efficiency.