Deep Unfolding Network For Block-Sparse Signal Recovery

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.