ELM-based Timing Synchronization for OFDM Systems by Exploiting Computer-aided Training Strategy

TL;DR

This paper introduces an ELM-based timing synchronization method for OFDM systems that uses a computer-aided training strategy to generate training data locally, improving robustness and generalization in realistic wireless channels.

Contribution

It proposes a novel training strategy leveraging channel impulse response information to enhance training data completeness for ELM-based synchronization.

Findings

The method improves synchronization robustness under varying conditions.

It enhances the generalization performance of the ELM-based TS network.

Numerical results confirm the scheme's effectiveness in realistic scenarios.

Abstract

Due to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning-based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this bottleneck, we extend the computer-aided approach, with which the local device can generate the training data instead of generating learning labels from the received samples collected in realistic systems, and then construct an extreme learning machine (ELM)-based TS network in orthogonal frequency division multiplexing (OFDM) systems. Specifically, by leveraging the rough information of channel impulse responses (CIRs), i.e., root-mean-square (r.m.s) delay, we propose the loose constraint-based and flexible constraint-based training strategies for the learning-label design against the maximum multi-path delay. The underlying mechanism is to improve the completeness of multi-path delays that may appear in the realistic wireless channels and thus increase the statistical efficiency of the designed TS learner. By this means, the proposed ELM-based TS network can alleviate the degradation of generalization performance. Numerical results reveal the robustness and generalization of the proposed scheme against varying parameters.