Lightweight 1-D CNN-based Timing Synchronization for OFDM Systems with CIR Uncertainty

TL;DR

This paper introduces a lightweight 1-D CNN-based timing synchronization method for OFDM systems that reduces complexity and delay while maintaining accuracy, and improves robustness against CIR uncertainty.

Contribution

It presents a novel lightweight CNN architecture transforming TS into a classification task, with enhanced generalization for CIR uncertainty in OFDM systems.

Findings

Improves timing synchronization accuracy in OFDM systems.

Reduces computational complexity and processing delay.

Demonstrates robustness against CIR uncertainty.

Abstract

In this letter, a lightweight one-dimensional convolutional neural network (1-D CNN)-based timing synchronization (TS) method is proposed to reduce the computational complexity and processing delay and hold the timing accuracy in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the TS task is first transformed into a deep learning (DL)-based classification task, and then three iterations of the compressed sensing (CS)-based TS strategy are simplified to form a lightweight network, whose CNN layers are specially designed to highlight the classification features. Besides, to enhance the generalization performance of the proposed method against the channel impulse responses (CIR) uncertainty, the relaxed restriction for propagation delay is exploited to augment the completeness of training data. Numerical results reflect that the proposed 1-D CNN-based TS method effectively improves the TS accuracy, reduces the computational complexity and processing delay, and possesses a good generalization performance against the CIR uncertainty. The source codes of the proposed method are available at https://github.com/qingchj851/CNNTS.