  The requirement of high spectrum efficiency puts forward higher requirements
on frame synchronization (FS) in wireless communication systems. Meanwhile, a
large number of nonlinear devices or blocks will inevitably cause nonlinear
distortion. To avoid the occupation of bandwidth resources and overcome the
difficulty of nonlinear distortion, an extreme learning machine (ELM)-based
network is introduced into the superimposed training-based FS with nonlinear
distortion. Firstly, a preprocessing procedure is utilized to reap the features
of synchronization metric (SM). Then, based on the rough features of SM, an ELM
network is constructed to estimate the offset of frame boundary. The analysis
and experiment results show that, compared with existing methods, the proposed
method can improve the error probability of FS and bit error rate (BER) of
symbol detection (SD). In addition, this improvement has its robustness against
the impacts of parameter variations.
