信息学院学术报告(2019年第11讲:)Sparse Bayesian Learning Using Approximate Message Passing with Unitary Transformation-首页 - 宁波大学

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信息学院学术报告(2019年第11讲:)Sparse Bayesian Learning Using Approximate Message Passing with Unitary Transformation

发布日期:2019-07-22 作者:信息科学与工程学院 文章来源:  责任编辑: 浏览量:

报告人: 郭庆华教授

报告时间:2019年7月22日上午9点

报告地点:包玉书7号楼210会议室

摘要:The conventional sparse Bayesian learning (SBL) algorithm suffers from high computational complexity. Recently, SBL has been 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 significantly slowing the convergence rate. In this talk, I will introduce a new low complexity SBL algorithm, which is designed based on the AMP with unitary transformation (UTAMP). I will show that, compared to state-of-the-art AMP based SBL algorithms, our proposed UTAMP-SBL is much more robust and converges much faster, leading to remarkably better performance. In many cases, the performance of the algorithm can approach the support-Oracle MMSE bound closely.


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