Institute for Communication Technologies and Embedded Systems

Lightweight Network and Model Aggregation for Automatic Modulation Classification

Authors:
Fu, X. ,  Gui, G. ,  Wang, Y. ,  Ohtsuki, T. ,  Abedisi, B. ,  Gacanin, H. ,  Adachi, F.
Book Title:
2021 IEEE Wireless Communications and Networking Conference (WCNC)
Date:
Apr. 2021
DOI:
10.1109/WCNC49053.2021.9417592
hsb:
RWTH-2021-11646
Language:
English
Abstract:
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.
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