A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices
Designing a denoising framework for high-mobility environments is challenging due to the limited size of collected data and low latency requirements. In this paper, we introduce a neural network (NN)-assisted denoiser for sparse signals in the frequency domain, referred to as dssNET, based on the lo...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10806718/ |
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| author | Wanjei Cho Seong-Cheol Kim Woong-Hee Lee |
| author_facet | Wanjei Cho Seong-Cheol Kim Woong-Hee Lee |
| author_sort | Wanjei Cho |
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| description | Designing a denoising framework for high-mobility environments is challenging due to the limited size of collected data and low latency requirements. In this paper, we introduce a neural network (NN)-assisted denoiser for sparse signals in the frequency domain, referred to as dssNET, based on the low-rank property of the transformed Hankel matrices constructed from sparse signals. The proposed method is based on optimizing the NN model by inputting singular values of the noisy transformed Hankel matrices and outputting the ground truth singular values. Furthermore, we additionally propose the advanced version of dssNET, referred to as selective dssNET (sdssNET), which can be operated more adaptively with the current signal-to-noise ratio (SNR). Notably, the proposed schemes show excellent denoising performance while requiring an extremely small training dataset compared to conventional schemes. Finally, we provide an application of joint-range-and-velocity estimation in automotive radar systems to validate the benefit of our proposed method in practical scenarios. |
| format | Article |
| id | doaj-art-fe80186c432648c6ae67a83c4f112bc8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fe80186c432648c6ae67a83c4f112bc82025-08-20T01:57:47ZengIEEEIEEE Access2169-35362024-01-011219299019300010.1109/ACCESS.2024.351958010806718A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel MatricesWanjei Cho0https://orcid.org/0009-0000-5136-4005Seong-Cheol Kim1https://orcid.org/0000-0002-7896-5625Woong-Hee Lee2https://orcid.org/0000-0002-1064-5123Department of Electrical and Computer Engineering and INMC, Seoul National University, Seoul, Republic of KoreaDepartment of Electrical and Computer Engineering and INMC, Seoul National University, Seoul, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul, Republic of KoreaDesigning a denoising framework for high-mobility environments is challenging due to the limited size of collected data and low latency requirements. In this paper, we introduce a neural network (NN)-assisted denoiser for sparse signals in the frequency domain, referred to as dssNET, based on the low-rank property of the transformed Hankel matrices constructed from sparse signals. The proposed method is based on optimizing the NN model by inputting singular values of the noisy transformed Hankel matrices and outputting the ground truth singular values. Furthermore, we additionally propose the advanced version of dssNET, referred to as selective dssNET (sdssNET), which can be operated more adaptively with the current signal-to-noise ratio (SNR). Notably, the proposed schemes show excellent denoising performance while requiring an extremely small training dataset compared to conventional schemes. Finally, we provide an application of joint-range-and-velocity estimation in automotive radar systems to validate the benefit of our proposed method in practical scenarios.https://ieeexplore.ieee.org/document/10806718/Signal denoisingsparse signalsneural networks |
| spellingShingle | Wanjei Cho Seong-Cheol Kim Woong-Hee Lee A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices IEEE Access Signal denoising sparse signals neural networks |
| title | A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices |
| title_full | A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices |
| title_fullStr | A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices |
| title_full_unstemmed | A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices |
| title_short | A Neural Network-Assisted Denoiser for Sparse Signals With Low Rank Property of Transformed Hankel Matrices |
| title_sort | neural network assisted denoiser for sparse signals with low rank property of transformed hankel matrices |
| topic | Signal denoising sparse signals neural networks |
| url | https://ieeexplore.ieee.org/document/10806718/ |
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