Dive Into Deep Learning Based Automatic Modulation Classification: A Disentangled Approach
Xiaolei Shang, Honglin Hu, Xiaoqiang Li, Tianheng Xu, Ting Zhou
Abstract
Recently, deep learning (DL) based automatic modulation classification (AMC) has received much attention. Various network structures with higher complexity are utilized to boost the performance of classification model. We divide the issue of AMC into two objectives and propose a disentangled approach with a signal processing module. Unlike popular end-to-end training strategy, we first consider a simple model with much fewer trainable parameters to learn accurate modulation features for classification. Then a U-net based signal processing module using a specially designed function is introduced to transfer the knowledge stored in classification module. We compare the performance of the proposed method with several baseline models on two well known datasets. Experimental results demonstrate that the proposed method gives superior performance with lower computational complexity compared with other methods. Furthermore, we also verify the feasibility and huge potential of the knowledge transferring in the field of wireless communications.