  Multiple-input multiple-output orthogonal frequency-division multiplexing
(MIMO-OFDM) is a key technology component in the evolution towards cognitive
radio (CR) in next-generation communication in which the accuracy of timing and
frequency synchronization significantly impacts the overall system performance.
In this paper, we propose a novel scheme leveraging extreme learning machine
(ELM) to achieve high-precision synchronization. Specifically, exploiting the
preamble signals with synchronization offsets, two ELMs are incorporated into a
traditional MIMO-OFDM system to estimate both the residual symbol timing offset
(RSTO) and the residual carrier frequency offset (RCFO). The simulation results
show that the performance of the proposed ELM-based synchronization scheme is
superior to the traditional method under both additive white Gaussian noise
(AWGN) and frequency selective fading channels. Furthermore, comparing with the
existing machine learning based techniques, the proposed method shows
outstanding performance without the requirement of perfect channel state
information (CSI) and prohibitive computational complexity. Finally, the
proposed method is robust in terms of the choice of channel parameters (e.g.,
number of paths) and also in terms of "generalization ability" from a machine
learning standpoint.
