Automatic modulation classification (AMC) is one of the most important technologies in various communication systems, including drone communications. It can be applied to confirm the legitimacy of access devices, help drone systems better identify and track signals from other communication devices, and prevent drone interference to ensure the safety and reliability of communication. However, the classification performance of previously proposed AMC approaches still needs to be improved. In this study, a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC approach is proposed to enhance the classification accuracy for the purpose of aiding drone communication because SDDFNN can effectively mine spatiotemporal features from modulation signals through residual modules, long-short term memory (LSTM) modules, and attention mechanisms. In addition, a novel hybrid data augmentation method based on phase shift and self-perturbation is introduced to further improve performance and avoid overfitting. The experimental results demonstrate that the proposed AMC approach can achieve an average classification accuracy of 63.44%, and the maximum accuracy can reach 95.01% at SNR = 10 dB, which outperforms the previously proposed methods.


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    Title :

    Hybrid Data Augmentation and Dual-Stream Spatiotemporal Fusion Neural Network for Automatic Modulation Classification in Drone Communications


    Contributors:
    An Gong (author) / Xingyu Zhang (author) / Yu Wang (author) / Yongan Zhang (author) / Mengyan Li (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown