Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning
Abstract Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unaccept...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
|
| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12157 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546739169525760 |
|---|---|
| author | Di Zhang Yimao Sun Siqi Bai Qun Wan |
| author_facet | Di Zhang Yimao Sun Siqi Bai Qun Wan |
| author_sort | Di Zhang |
| collection | DOAJ |
| description | Abstract Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity. |
| format | Article |
| id | doaj-art-776638fcb6424467888012a745821ed2 |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-776638fcb6424467888012a745821ed22025-02-03T06:47:26ZengWileyIET Signal Processing1751-96751751-96832022-12-011691118112910.1049/sil2.12157Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learningDi Zhang0Yimao Sun1Siqi Bai2Qun Wan3School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu ChinaCollege of Computer Science Sichuan University Chengdu ChinaCollege of Communication Engineering Chengdu University of Information Technology Chengdu ChinaSchool of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu ChinaAbstract Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity.https://doi.org/10.1049/sil2.12157adaptive coupled patternblock sparsityphase retrievalvariational Bayesian learning (VBL) |
| spellingShingle | Di Zhang Yimao Sun Siqi Bai Qun Wan Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning IET Signal Processing adaptive coupled pattern block sparsity phase retrieval variational Bayesian learning (VBL) |
| title | Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning |
| title_full | Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning |
| title_fullStr | Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning |
| title_full_unstemmed | Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning |
| title_short | Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning |
| title_sort | phase retrieval for block sparsity based on adaptive coupled variational bayesian learning |
| topic | adaptive coupled pattern block sparsity phase retrieval variational Bayesian learning (VBL) |
| url | https://doi.org/10.1049/sil2.12157 |
| work_keys_str_mv | AT dizhang phaseretrievalforblocksparsitybasedonadaptivecoupledvariationalbayesianlearning AT yimaosun phaseretrievalforblocksparsitybasedonadaptivecoupledvariationalbayesianlearning AT siqibai phaseretrievalforblocksparsitybasedonadaptivecoupledvariationalbayesianlearning AT qunwan phaseretrievalforblocksparsitybasedonadaptivecoupledvariationalbayesianlearning |