Metric Learning-Based Timing Synchronization by Using Lightweight Neural Network

TL;DR

This paper introduces a lightweight neural network-based timing synchronization method for OFDM systems that effectively addresses multi-path uncertainty, improving synchronization accuracy through learned metric features.

Contribution

It proposes a novel timing-metric learning approach using a 1-D CNN to enhance synchronization accuracy under multi-path conditions, with a new training objective considering multi-path characteristics.

Findings

Improved synchronization accuracy under multi-path uncertainty.

Enhanced generalization of the timing synchronization method.

Effective metric feature extraction with lightweight neural network.

Abstract

Timing synchronization (TS) is one of the key tasks in orthogonal frequency division multiplexing (OFDM) systems. However, multi-path uncertainty corrupts the TS correctness, making OFDM systems suffer from a severe inter-symbol-interference (ISI). To tackle this issue, we propose a timing-metric learning-based TS method assisted by a lightweight one-dimensional convolutional neural network (1-D CNN). Specifically, the receptive field of 1-D CNN is specifically designed to extract the metric features from the classic synchronizer. Then, to combat the multi-path uncertainty, we employ the varying delays and gains of multi-path (the characteristics of multi-path uncertainty) to design the timing-metric objective, and thus form the training labels. This is typically different from the existing timing-metric objectives with respect to the timing synchronization point. Our method substantively increases the completeness of training data against the multi-path uncertainty due to the complete preservation of metric information. By this mean, the TS correctness is improved against the multi-path uncertainty. Numerical results demonstrate the effectiveness and generalization of the proposed TS method against the multi-path uncertainty.