| Paper ID | SPTM-24.3 |
| Paper Title |
GENERAL TOTAL VARIATION REGULARIZED SPARSE BAYESIAN LEARNING FOR ROBUST BLOCK-SPARSE SIGNAL RECOVERY |
| Authors |
Aditya Sant, University of California, San Diego, United States; Markus Leinonen, University of Oulu, Finland; Bhaskar D. Rao, University of California, San Diego, United States |
| Session | SPTM-24: Sparsity-aware Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SMDSP-SAP] Sparsity-aware Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
