Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in drone communication systems. Recently, benefiting 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 a local framework (LocalAMC) where there is only one device, or a centralized framework (CentAMC) where multiple local devices (LDs) upload their data to only one central server (CS). LocalAMC may not achieve 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 are quite massive. In this paper, we propose a practical and light AMC method based on decentralized learning with residual network (ResNet) in drone communication systems. Simulation results show that the ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.


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    Title :

    Decentralized Learning and Model Averaging Based Automatic Modulation Classification in Drone Communication Systems


    Contributors:
    Min Ma (author) / Yunhe Xu (author) / Zhi Wang (author) / Xue Fu (author) / Guan Gui (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






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