Sparse Bayesian learning based on approximate message passing with unitary transformation

Man Luo, Qinghua Guo, Defeng Huang, Jiangtao Xi

Research output: Chapter in Book/Conference paperConference paperpeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781728112046
DOIs
Publication statusPublished - Aug 2019
Event2019 IEEE VTS Asia Pacific Wireless Communications Symposium - Singapore, Singapore
Duration: 28 Aug 201930 Aug 2019

Publication series

NameProceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019

Conference

Conference2019 IEEE VTS Asia Pacific Wireless Communications Symposium
Abbreviated title APWCS 2019
Country/TerritorySingapore
CitySingapore
Period28/08/1930/08/19

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