Most current deep learning (DL) based AMC methods often require a large number of labeled samples to drive itself optimization study, so as to achieve a more superior performance. However, for many AMC tasks with a small amount of data, the AMC method based on deep learning still has many challenges, which makes the deep neural network model trained on a small dataset lack effective migration effect, leading to the failure to achieve better performance effect in the testing process. In this paper, we propose a novel AMC method based on Complex-valued Signal Capsule Network (CSCN). Compared with other DL based methods, the proposed CSCN can achieve a better performance under the condition of small dataset and low SNR on the well-known deepsig dataset RML2016.10a and RML2016.10b.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Complex-Valued Signal Capsule Network for Automatic Modulation Classification


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Yang, Shuyuan (author) / Feng, Zhixi (author) / Wang, Nengguo (author) / Bu, Yunong (author) / Hu, Hao (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    6 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Complex-Valued Neural Network Detection for RIS-Assisted Generalized Spatial Modulation

    Liu, Yuyan / Zhang, Chaorong / Ng, Benjamin K. et al. | IEEE | 2024



    Vehicle-Type Classification Using Capsule Neural Network

    Mane, Deepak / Kharche, Chaitanya / Bankar, Shweta et al. | Springer Verlag | 2022



    Neural Network Based Automatic Modulation Classification with Online Training

    Zhang, Shuo / Yakopcic, Chris / Taha, Tarek M. | IEEE | 2023