Label Design-based ELM Network for Timing Synchronization in OFDM Systems with Nonlinear Distortion

TL;DR

This paper introduces an ELM-based network with a novel label design to improve timing synchronization in OFDM systems affected by nonlinear distortion, enhancing accuracy and robustness across various channel conditions.

Contribution

The work proposes a new label design and an ELM-based network for OFDM timing synchronization, addressing nonlinear distortion effects and improving estimation accuracy.

Findings

Enhanced timing synchronization performance in OFDM systems.

Effective generalization across different channel scenarios.

Improved estimation within ISI-free regions.

Abstract

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.