Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3479 |
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| author | Gyung-Eun Kim Jung-Hwan Kim Jong-Ho Lee Woong-Hee Lee |
| author_facet | Gyung-Eun Kim Jung-Hwan Kim Jong-Ho Lee Woong-Hee Lee |
| author_sort | Gyung-Eun Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9365e092cd32432b926db7acb8037cb6 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9365e092cd32432b926db7acb8037cb62025-08-20T03:06:20ZengMDPI AGApplied Sciences2076-34172025-03-01157347910.3390/app15073479Neural-Network-Based Synchronization Acquisition with Hankelization PreprocessingGyung-Eun Kim0Jung-Hwan Kim1Jong-Ho Lee2Woong-Hee Lee3Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Republic of KoreaSchool of Electronic Engineering, Soongsil University, Seoul 06978, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Republic of KoreaConventional 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.https://www.mdpi.com/2076-3417/15/7/3479synchronization acquisitionneural networkZadoff–Chu sequencebinary classificationHankelization |
| spellingShingle | Gyung-Eun Kim Jung-Hwan Kim Jong-Ho Lee Woong-Hee Lee Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing Applied Sciences synchronization acquisition neural network Zadoff–Chu sequence binary classification Hankelization |
| title | Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing |
| title_full | Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing |
| title_fullStr | Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing |
| title_full_unstemmed | Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing |
| title_short | Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing |
| title_sort | neural network based synchronization acquisition with hankelization preprocessing |
| topic | synchronization acquisition neural network Zadoff–Chu sequence binary classification Hankelization |
| url | https://www.mdpi.com/2076-3417/15/7/3479 |
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