Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
Year: 2025
Type: article
Abstract: Conventional synchronization signal detection methods rely on linear correlation function analysis with fixed thresholds, which are insufficient for handling the nonlinear characteristics of practical wireless communication systems. In such environments, the usage of a long synchronization signal is beneficial for ensuring sufficient correlation information and enhancing detection robustness. To address these problems, this paper proposes a novel framework that combines Hankelization-based preprocessing with the operation of a neural network (NN). The proposed method enhances feature extraction through the inverse Fourier transform and Hankel matrix construction, followed by singular value decomposition (SVD) to preserve dominant signal features and suppress noise components. Leveraging the ability of NNs to learn nonlinear patterns, the proposed method eliminates the need for fixed thresholds and achieves robust synchronization signal detection. The simulation results demonstrate superior accuracy in various environments compared to conventional methods, underscoring the potential of Hankelization-based preprocessing in future wireless communication systems.
Source: Applied Sciences
DOI: 10.3390/app15073479
Institutions: Dongguk University, Soongsil University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4408780504