  Due to the nonlinear distortion in Orthogonal frequency division multiplexing
(OFDM) systems, the timing synchronization (TS) performance is inevitably
degraded at the receiver. To relieve this issue, an extreme learning machine
(ELM)-based network with a novel learning label is proposed to the TS of OFDM
system in our work and increases the possibility of symbol timing offset (STO)
estimation residing in inter-symbol interference (ISI)-free region. Especially,
by exploiting the prior information of the ISI-free region, two types of
learning labels are developed to facilitate the ELM-based TS network. With
designed learning labels, a timing-processing by classic TS scheme is first
executed to capture the coarse timing metric (TM) and then followed by an ELM
network to refine the TM. According to experiments and analysis, our scheme
shows its effectiveness in the improvement of TS performance and reveals its
generalization performance in different training and testing channel scenarios.
