Joint Sparse Recovery Using Deep Unfolding With Application to Massive Random Access
- Resource Type
- Conference
- Authors
- Sabulal, Anand P.; Bhashyam, Srikrishna
- Source
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020 - 2020 IEEE International Conference on. :5050-5054 May, 2020
- Subject
- Signal Processing and Analysis
Conferences
Signal processing algorithms
Estimation
Signal processing
Iterative algorithms
Speech processing
Convergence
- Language
- ISSN
- 2379-190X
We propose a learning-based joint sparse recovery method for the multiple measurement vector (MMV) problem using deep unfolding. We unfold an iterative alternating direction method of multipliers (ADM) algorithm for MMV joint sparse recovery algorithm into a trainable deep network. This ADM algorithm is first obtained by modifying the squared error penalty function of an existing ADM algorithm to a back-projected squared error penalty function. Numerical results for a massive random access system show that our proposed modification to the MMV-ADM method and deep unfolding provide significant improvement in convergence and estimation performance.