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.
Improved Deep Learning in OFDM Systems With Imperfect Timing Synchronization
01.05.2020
324878 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Robust timing synchronization for asymmetrically clipped OFDM based optical wireless communications
IEEE | 2015
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