The paper proposes a deep neural network (DNN) based receiver to outperform the state-of-the-art w/o timing synchronization error in orthogonal frequency-division multiplexing (OFDM) systems. Moreover, the closed-form of a traditional minimum mean square error (MMSE) receiver is derived in the presence of inter-symbol-interference. The derived receiver is used to benchmark the performance of the proposed DNN.


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    Titel :

    Improved Deep Learning in OFDM Systems With Imperfect Timing Synchronization


    Beteiligte:
    He, Ziming (Autor:in) / Huang, Xuan (Autor:in)


    Erscheinungsdatum :

    01.05.2020


    Format / Umfang :

    324878 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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