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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    Title :

    Improved Deep Learning in OFDM Systems With Imperfect Timing Synchronization


    Contributors:
    He, Ziming (author) / Huang, Xuan (author)


    Publication date :

    2020-05-01


    Size :

    324878 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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