  Timing synchronization (TS) is one of the key tasks in orthogonal frequency
division multiplexing (OFDM) systems. However, multi-path uncertainty corrupts
the TS correctness, making OFDM systems suffer from a severe
inter-symbol-interference (ISI). To tackle this issue, we propose a
timing-metric learning-based TS method assisted by a lightweight
one-dimensional convolutional neural network (1-D CNN). Specifically, the
receptive field of 1-D CNN is specifically designed to extract the metric
features from the classic synchronizer. Then, to combat the multi-path
uncertainty, we employ the varying delays and gains of multi-path (the
characteristics of multi-path uncertainty) to design the timing-metric
objective, and thus form the training labels. This is typically different from
the existing timing-metric objectives with respect to the timing
synchronization point. Our method substantively increases the completeness of
training data against the multi-path uncertainty due to the complete
preservation of metric information. By this mean, the TS correctness is
improved against the multi-path uncertainty. Numerical results demonstrate the
effectiveness and generalization of the proposed TS method against the
multi-path uncertainty.
