A. Sant, M. Leinonen and B. D. Rao, "Block-Sparse Signal Recovery via General Total Variation Regularized Sparse Bayesian Learning," in IEEE Transactions on Signal Processing, vol. 70, pp. 1056-1071, 2022, doi: 10.1109/TSP.2022.3144948
Block-sparse signal recovery via general total variation regularized sparse Bayesian learning
|Author:||Sant, Aditya1; Leinonen, Markus2; Rao, Bhaskar D.1|
1Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, USA
2Centre for Wireless Communications – Radio Technologies, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022051736128
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-05-17
One of the main challenges in block-sparse signal recovery, as encountered in, e.g., multi-antenna mmWave channel models, is block-patterned estimation without knowledge of block sizes and boundaries. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse signal recovery under unknown block patterns. Contrary to conventional approaches that impose block-promoting regularization on the signal components, we apply two classes of hyperparameter regularizers for the SBL cost function, inspired by total variation (TV) denoising. The first class relies on a conventional TV difference unit and allows performing the SBL inference iteratively through a set of convex optimization problems, enabling a flexible choice of numerical solvers. The second class incorporates a region-aware TV penalty to penalize the signal and zero blocks in a dissimilar manner, enhancing the performance. We derive an alternating optimization algorithm based on expectation-maximization to perform the SBL inference through computationally efficient parallel updates for both the regularizer classes. The numerical results show that the proposed TV-regularized SBL algorithm is robust to the nature of the block structure and is capable of recovering signals with both block-patterned and isolated components, proving effective for various signal recovery systems.
IEEE transactions on signal processing
|Pages:||1056 - 1071|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work of Aditya Sant and Bhaskar D. Rao was supported in part by ONR under Grant N00014-18-1-2038, in part by NSF under Grant CCF-2124929, and in part by the UCSD Center for Wireless Communications. The work of Markus Leinonen was supported in part by the Walter Ahlström Foundation through Tutkijat Maailmalle Program, Infotech Oulu, the Academy of Finland under Grants 323698 and 319485, and in part by the Academy of Finland 6Genesis Flagship under Grant 318927. A part of this work was presented at the ICASSP 2021. [DOI: 10.1109/ICASSP39728.2021.9413977].
|Academy of Finland Grant Number:||
323698 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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