In recent years, extensive research has been done to obtain better demodulation performance by combining signal demodulation with deep learning algorithms, such as convolutional neural networks (CNN). However, the demodulation performance of CNN is not much improved compared with traditional coherent demodulation algorithms. This paper proposes a signal demodulation algorithm based on generative adversarial networks (GAN). The proposed GAN-based demodulation network uses the confrontation characteristics between the generator and the discriminator in GAN to greatly improve the demodulation ability of the signal. Numerical results show that the demodulation performance is better than the coherent demodulation algorithm and the CNN-based demodulation algorithm.


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    Title :

    A Signal Demodulation Algorithm Based on Generative Adversarial Networks


    Contributors:
    Lijun, Wang (author) / Minghong, Zhang (author) / Yu, Han (author) / Lei, Shen (author)


    Publication date :

    2021-10-20


    Size :

    1522647 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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