Blind Deconvolution of Nonstationary Graph Signals over Shift-Invariant Channels
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911119027732480 |
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| author | Zare, Ali Shi, Yao Sun, Qiyu |
| author_facet | Zare, Ali Shi, Yao Sun, Qiyu |
| contents | In this paper, we investigate blind deconvolution of nonstationary graph signals from noisy observations, transmitted through an unknown shift-invariant channel. The deconvolution process assumes that the observer has access to the covariance structure of the original graph signals. To evaluate the effectiveness of our channel estimation and blind deconvolution method, we conduct numerical experiments using a temperature dataset in the Brest region of France. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17210 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blind Deconvolution of Nonstationary Graph Signals over Shift-Invariant Channels Zare, Ali Shi, Yao Sun, Qiyu Information Theory Signal Processing In this paper, we investigate blind deconvolution of nonstationary graph signals from noisy observations, transmitted through an unknown shift-invariant channel. The deconvolution process assumes that the observer has access to the covariance structure of the original graph signals. To evaluate the effectiveness of our channel estimation and blind deconvolution method, we conduct numerical experiments using a temperature dataset in the Brest region of France. |
| title | Blind Deconvolution of Nonstationary Graph Signals over Shift-Invariant Channels |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2508.17210 |