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Multi-path fading seriously affects the accuracy of timing synchronization(TS) in orthogonal frequency division multiplexing (OFDM) systems. To tacklethis issue, we propose a convolutional neural network (CNN)-based TS schemeassisted by initial path acquisition in this paper. Specifically, the classiccross-correlation method is first employed to estimate a coarse timing offsetand capture an initial path, which shrinks the TS search region. Then, aone-dimensional (1-D) CNN is developed to optimize the TS of OFDM systems. Dueto the narrowed search region of TS, the CNN-based TS effectively locates theaccurate TS point and inspires us to construct a lightweight network in termsof computational complexity and online running time. Compared with thecompressed sensing-based TS method and extreme learning machine-based TSmethod, simulation results show that the proposed method can effectivelyimprove the TS performance with the reduced computational complexity and onlinerunning time. Besides, the proposed TS method presents robustness against thevariant parameters of multi-path fading channels.

CNN-based Timing Synchronization for OFDM Systems Assisted by Initial Path Acquisition in Frequency Selective Fading Channel