Sparse Bayesian learning based on approximate message passing with unitary transformation

Authors

Man Luo, University of WollongongFollow
Qinghua Guo, University of WollongongFollow
Defeng Huang
Jiangtao Xi, University of WollongongFollow

RIS ID

139622

Publication Details

M. Luo, Q. Guo, D. Huang & J. Xi, "Sparse Bayesian learning based on approximate message passing with unitary transformation," in Proceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019, 2019,

Abstract

2019 IEEE. Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it is vulnerable to 'difficult' measurement matrices as AMP can easily diverge. Damped AMP has been used to alleviate the problem at the cost of slowing the convergence speed. In this work, we propose an SBL algorithm based on the AMP with unitary transformation (UTAMP). It is shown that, compared to the state-of-the-art AMP based SBL algorithm, the proposed UTAMP-SBL is much more robust and much faster, leading to remarkably better performance. It is shown that in many cases, UTAMP-SBL can approach the support-oracle bound closely.