  Due to the implementation bottleneck of training data collection in realistic
wireless communications systems, supervised learning-based timing
synchronization (TS) is challenged by the incompleteness of training data. To
tackle this bottleneck, we extend the computer-aided approach, with which the
local device can generate the training data instead of generating learning
labels from the received samples collected in realistic systems, and then
construct an extreme learning machine (ELM)-based TS network in orthogonal
frequency division multiplexing (OFDM) systems. Specifically, by leveraging the
rough information of channel impulse responses (CIRs), i.e., root-mean-square
(r.m.s) delay, we propose the loose constraint-based and flexible
constraint-based training strategies for the learning-label design against the
maximum multi-path delay. The underlying mechanism is to improve the
completeness of multi-path delays that may appear in the realistic wireless
channels and thus increase the statistical efficiency of the designed TS
learner. By this means, the proposed ELM-based TS network can alleviate the
degradation of generalization performance. Numerical results reveal the
robustness and generalization of the proposed scheme against varying
parameters.
