ELM-based Frame Synchronization in Nonlinear Distortion Scenario Using Superimposed Training

TL;DR

This paper introduces an ELM-based approach for frame synchronization in wireless systems affected by nonlinear distortion, enhancing accuracy and robustness using superimposed training.

Contribution

It proposes a novel ELM-based network for FS that effectively handles nonlinear distortion without bandwidth overhead, outperforming existing methods.

Findings

Improves frame synchronization error probability

Reduces bit error rate in symbol detection

Demonstrates robustness against parameter variations

Abstract

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.