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
2020-05-01
324878 byte
Conference paper
Electronic Resource
English
Robust timing synchronization for asymmetrically clipped OFDM based optical wireless communications
IEEE | 2015
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