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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.11805 |
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| _version_ | 1866914647390552064 |
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| author | Wang, Haifeng Chen, Jinchi Fan, Hulei Zhao, Yuxiang Yu, Li |
| author_facet | Wang, Haifeng Chen, Jinchi Fan, Hulei Zhao, Yuxiang Yu, Li |
| contents | In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this problem can be reformulated as a low-rank matrix demixing problem. We propose a convex recovery approach that utilizes the low-rank structure of each vectorized Hankel matrix associated with the target matrix. Our analysis reveals that for achieving exact recovery, the number of samples needs to satisfy the condition $n\gtrsim Ksr \log (sn)$. Empirical evaluations demonstrate the recovery capabilities and the computational efficiency of the convex method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11805 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift Wang, Haifeng Chen, Jinchi Fan, Hulei Zhao, Yuxiang Yu, Li Information Theory In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this problem can be reformulated as a low-rank matrix demixing problem. We propose a convex recovery approach that utilizes the low-rank structure of each vectorized Hankel matrix associated with the target matrix. Our analysis reveals that for achieving exact recovery, the number of samples needs to satisfy the condition $n\gtrsim Ksr \log (sn)$. Empirical evaluations demonstrate the recovery capabilities and the computational efficiency of the convex method. |
| title | Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift |
| topic | Information Theory |
| url | https://arxiv.org/abs/2401.11805 |