  In this letter, a lightweight one-dimensional convolutional neural network
(1-D CNN)-based timing synchronization (TS) method is proposed to reduce the
computational complexity and processing delay and hold the timing accuracy in
orthogonal frequency division multiplexing (OFDM) systems. Specifically, the TS
task is first transformed into a deep learning (DL)-based classification task,
and then three iterations of the compressed sensing (CS)-based TS strategy are
simplified to form a lightweight network, whose CNN layers are specially
designed to highlight the classification features. Besides, to enhance the
generalization performance of the proposed method against the channel impulse
responses (CIR) uncertainty, the relaxed restriction for propagation delay is
exploited to augment the completeness of training data. Numerical results
reflect that the proposed 1-D CNN-based TS method effectively improves the TS
accuracy, reduces the computational complexity and processing delay, and
possesses a good generalization performance against the CIR uncertainty. The
source codes of the proposed method are available at
https://github.com/qingchj851/CNNTS.
