A Novel Deep Learning-Based Coarse-to-Fine Frame Synchronization Method for OTFS Systems

TL;DR

This paper introduces a deep learning-based coarse-to-fine synchronization method for OTFS systems that improves accuracy and reduces complexity in high-mobility wireless environments by exploiting delay-Doppler domain features.

Contribution

It presents a novel hierarchical deep residual network architecture for OTFS frame synchronization that outperforms traditional methods in accuracy and efficiency.

Findings

Achieves high synchronization accuracy in low SNR conditions.

Reduces computational complexity compared to conventional algorithms.

Demonstrates robustness in diverse channel scenarios.

Abstract

Orthogonal time frequency space (OTFS) modulation is a robust candidate waveform for future wireless systems, particularly in high-mobility scenarios, as it effectively mitigates the impact of rapidly time-varying channels by mapping symbols in the delay-Doppler (DD) domain. However, accurate frame synchronization in OTFS systems remains a challenge due to the performance limitations of conventional algorithms. To address this, we propose a low-complexity synchronization method based on a coarse-to-fine deep residual network (ResNet) architecture. Unlike traditional approaches relying on high-overhead preamble structures, our method exploits the intrinsic periodic features of OTFS pilots in the delay-time (DT) domain to formulate synchronization as a hierarchical classification problem. Specifically, the proposed architecture employs a two-stage strategy to first narrow the search space and then pinpoint the precise symbol timing offset (STO), thereby significantly reducing computational complexity while maintaining high estimation accuracy. We construct a comprehensive simulation dataset incorporating diverse channel models and randomized STO to validate the method. Extensive simulation results demonstrate that the proposed method achieves robust signal start detection and superior accuracy compared to conventional benchmarks, particularly in low signal-to-noise ratio (SNR) regimes and high-mobility scenarios.