Automatic Modulation Classification Based on Decentralized Learning and Model Averaging

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in non-cooperative communication scenarios. Recently, benefitting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on local framework (LocalAMC) where there is only one device, or centralized framework (CentAMC) where multiple local devices (LDs) upload their data to the only one central server (CS). LocalAMC may not get ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS is quite massive. In this paper, we propose an AMC method based on decentralized learning with residual network (ResNet). Simulation results show that ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.