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Timing synchronization (TS) is vital for orthogonal frequency divisionmultiplexing (OFDM) systems, which makes the discrete Fourier transform (DFT)window start at the inter-symbol-interference (ISI)-free region. However, themulti-path uncertainty in wireless communication scenarios degrades the TScorrectness. To alleviate this degradation, we propose a learning-based TSmethod enhanced by improving the design of training label. In the proposedmethod, the classic cross-correlator extracts the initial TS feature forbenefiting the following machine learning. Wherein, the network architectureunfolds one classic cross-correlation process. Against the multi-pathuncertainty, a novel training label is designed by representing the ISI-freeregion and especially highlighting its approximate midpoint. Therein, thecloser to the region boundary of ISI-free the smaller label values are set,expecting to locate the maximum network output in ISI-free region with a highprobability. Then, to guarantee the correctness of labeling, we exploit thepriori information of line-of-sight (LOS) to form a LOS-aided labeling.Numerical results confirm that, the proposed training label effectivelyenhances the correctness of the proposed TS learner against the multi-pathuncertainty.

Improved Label Design for Timing Synchronization in OFDM Systems against Multi-path Uncertainty