Lightweight Network and Model Aggregation for Automatic Modulation Classification in Wireless Communications
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.
Authors 7
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, NJUPT, Nanjing, China
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, NJUPT, Nanjing, China
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, NJUPT, Nanjing, China
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Affiliation as printed
Keio University, Yokohama, Japan
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Manchester Metropolitan University
Affiliation as printed
Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom
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Affiliation as printed
Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Research Organization of Electrical Communication, Tohoku University, Sendai, Japan
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