Lightweight Network and Model Aggregation for Automatic Modulation Classification in Wireless Communications
Xue Gang Fu, Guan Gui, Yu Wang, Tomoaki Otsuki Ohtsuki, Bamidele Adebisi, Haris Gacanin, Fumiyuki Adachi · 2021
This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.