Clipping noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel clipping noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the clipping noise is determined by a deep complex network, namely clipping noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated clipping noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated clipping noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the clipping noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the clipping noise from noise-affected features.


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    Titel :

    Clipping Noise Estimation Based on Deep Complex Neural Network with Sparsity Constraint


    Beteiligte:
    Zhang, Xudong (Autor:in) / Zhang, Yu (Autor:in) / Chang, Xiaohua (Autor:in) / Wu, Yichen (Autor:in) / Pan, Changyong (Autor:in)


    Erscheinungsdatum :

    2020-05-01


    Format / Umfang :

    272487 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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