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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

  1. Nanjing University of Posts and Telecommunications

    Affiliation as printed

    College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

  2. Nanjing University of Posts and Telecommunications

    Affiliation as printed

    College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

  3. Nanjing University of Posts and Telecommunications

    Affiliation as printed

    College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

  4. Keio University

    Affiliation as printed

    Keio University, Yokohama, Japan

  5. Manchester Metropolitan University

    Affiliation as printed

    Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom

  6. RWTH Aachen University

    Affiliation as printed

    Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany

  7. Tohoku University

    Affiliation as printed

    Research Organization of Electrical Communication, Tohoku University, Sendai, Japan

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References 32