University of Oulu

A. Sant, M. Leinonen and B. D. Rao, "General Total Variation Regularized Sparse Bayesian Learning for Robust Block-Sparse Signal Recovery," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 5604-5608, doi: 10.1109/ICASSP39728.2021.9413977

General total variation regularized sparse Bayesian learning for robust block-sparse signal recovery

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Author: Sant, Aditya1; Leinonen, Markus2; Rao, Bhaskar D.1
Organizations: 1Department of Electrical and Computer Engineering, University of California San Diego
2Centre for Wireless Communications – Radio Technologies, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-06-29


Block-sparse signal recovery without knowledge of block sizes and boundaries, such as those encountered in multi-antenna mmWave channel models, is a hard problem for compressed sensing (CS) algorithms. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse recovery based on popular CS based regularizers with the function input variable related to total variation (TV). Contrary to conventional approaches that impose the regularization on the signal components, we regularize the SBL hyperparameters. This iterative TV-regularized SBL algorithm employs a majorization-minimization approach and reduces each iteration to a convex optimization problem, enabling a flexible choice of numerical solvers. The numerical results illustrate that the TV-regularized SBL algorithm is robust to the nature of the block structure and able to recover signals with both block-patterned and isolated components, proving useful for various signal recovery systems.

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Series: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
ISSN: 1520-6149
ISSN-E: 2379-190X
ISSN-L: 1520-6149
ISBN: 978-1-7281-7605-5
ISBN Print: 978-1-7281-7606-2
Pages: 1 - 5
DOI: 10.1109/ICASSP39728.2021.9413977
Host publication: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings. June 6–11, 2021 Virtual Conference Toronto, Ontario, Canada
Conference: IEEE International Conference on Acoustics, Speech, and Signal Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work of A. Sant and B. D. Rao has been financially supported by ONR Grant No. N00014-18-1-2038 and the UCSD Center forWireless Communications. The work of M. Leinonen has been financially supported in part by Walter Ahlström Foundation through Tutkijat Maailmalle program, Infotech Oulu, the Academy of Finland (grant 323698) and (grant 319485), and Academy of Finland 6Genesis Flagship (grant 318927).
Academy of Finland Grant Number: 323698
Detailed Information: 323698 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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