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
|Author:||Sant, Aditya1; Leinonen, Markus2; Rao, Bhaskar D.1|
1Department of Electrical and Computer Engineering, University of California San Diego
2Centre for Wireless Communications – Radio Technologies, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021062940372
Institute of Electrical and Electronics Engineers,
|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.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
|Pages:||1 - 5|
2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings. June 6–11, 2021 Virtual Conference Toronto, Ontario, Canada
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
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 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.