| Paper ID | MLSP-6.2 |
| Paper Title |
Deep Unfolding Network for Block-Sparse Signal Recovery |
| Authors |
Rong Fu, Tsinghua University, China; Vincent Monardo, Carnegie Mellon University, United States; Tianyao Huang, Yimin Liu, Tsinghua University, China |
| Session | MLSP-6: Compressed Sensing and Learning |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [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 has drawn increasing attention in many areas of signal processing, where the goal is to recover a high-dimensional signal whose non-zero coefficients only arise in a few blocks from compressive measurements. However, most off-the-shelf data-driven reconstruction networks do not exploit the block-sparse structure. Thus, they suffer from deteriorating performance in block-sparse signal recovery. In this paper, we put forward a block-sparse reconstruction network named Ada-BlockLISTA based on the concept of deep unfolding. Our proposed network consists of a gradient descent step on every single block followed by a block-wise shrinkage step. % with a trainable complex-valued shrinkage function. We evaluate the performance of the proposed Ada-BlockLISTA network through simulations based on the signal model of 2D harmonic retrieval problems. |
