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Unitary Approximate Message Passing for Sparse Bayesian Learning

Journal Article


Abstract


  • Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it does not work well for a generic measurement matrix, which may cause AMP to diverge. Damped AMP has been used for SBL to alleviate divergence issues at the cost of reducing convergence speed. In this work, we propose a new SBL algorithm based on structured variational inference, leveraging AMP with a unitary transformation. Both single measurement vector and multiple measurement vector problems are investigated. It is shown that, compared to state-of-the-art AMP-based SBL algorithms, the proposed UAMP-SBL is more robust and efficient, leading to remarkably better performance.

Publication Date


  • 2021

Citation


  • Luo, M., Guo, Q., Jin, M., Eldar, Y. C., Huang, D., & Meng, X. (2021). Unitary Approximate Message Passing for Sparse Bayesian Learning. IEEE Transactions on Signal Processing, 69, 6023-6039. doi:10.1109/TSP.2021.3114985

Scopus Eid


  • 2-s2.0-85115779422

Start Page


  • 6023

End Page


  • 6039

Volume


  • 69

Issue


Place Of Publication


Abstract


  • Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it does not work well for a generic measurement matrix, which may cause AMP to diverge. Damped AMP has been used for SBL to alleviate divergence issues at the cost of reducing convergence speed. In this work, we propose a new SBL algorithm based on structured variational inference, leveraging AMP with a unitary transformation. Both single measurement vector and multiple measurement vector problems are investigated. It is shown that, compared to state-of-the-art AMP-based SBL algorithms, the proposed UAMP-SBL is more robust and efficient, leading to remarkably better performance.

Publication Date


  • 2021

Citation


  • Luo, M., Guo, Q., Jin, M., Eldar, Y. C., Huang, D., & Meng, X. (2021). Unitary Approximate Message Passing for Sparse Bayesian Learning. IEEE Transactions on Signal Processing, 69, 6023-6039. doi:10.1109/TSP.2021.3114985

Scopus Eid


  • 2-s2.0-85115779422

Start Page


  • 6023

End Page


  • 6039

Volume


  • 69

Issue


Place Of Publication