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【12.30腾讯会议】An Efficient Sparse Quadratic Programming Relaxation Based Algorithm for Large-Scale MIMO Detection


报告题目:An Efficient Sparse Quadratic Programming Relaxation Based Algorithm for Large-Scale MIMO Detection

报告学长: Qingna Li(李庆娜) 


报告时间:2020年12月30日下午15:50—16:40


报告地点:腾讯会议 ID: 381 6014 5369


摘要:Multiple-input multiple-output (MIMO) detection is a fundamental problem in wireless communications and it is strongly NP-hard in general. Massive MIMO has been recognized as a key technology in the fifth generation (5G) and beyond communication networks, which on one hand can significantly improve the communication performance, and on the other hand poses new challenges of solving the corresponding optimization problems due to the large problem size. While various efficient algorithms such as semidefinite relaxation (SDR) based approaches have been proposed for solving the small-scale MIMO detection problem, they are not suitable to solve the large-scale MIMO detection problem due to their high computational complexities. In this paper, we propose an efficient sparse quadratic programming (SQP) relaxation based algorithm for solving the large-scale MIMO detection problem. In particular, we first reformulate the MIMO detection problem as an SQP problem. By dropping the sparse constraint, the resulting relaxation problem shares the same global minimizer with the SQP problem. In sharp contrast to the SDRs for the MIMO detection problem, our relaxation does not contain any (positive semidefinite) matrix variable and the numbers of variables and constraints in our relaxation are significantly less than those in the SDRs, which makes it particularly suitable for the large-scale problem. Then we propose a projected Newton based quadratic penalty method to solve the relaxation problem, which is guaranteed to converge to the transmitted vector of signals under reasonable conditions. By extensive numerical experiments, when applied to solve small-scale problems, the proposed algorithm is demonstrated to be competitive with the state-of-the-art approaches in terms of detection accuracy and solution efficiency; when applied to solve large-scale problems, the proposed algorithm achieves better detection performance and is more robust to the choice of the initial point than a recently proposed generalized power method.


报告人简介:Qingna Li received her Doctor’s degree in computational mathematics in 2010, Hunan University, and her Bachalor’s degree in 2005, Hunan University. She was a visiting student of University of Southampton, UK during 2008-2009. She was a Postdoc in Academy of Mathematics and System Sciences, Chinese Academy of Sciences during 2010-2012.

She joined Beijing Institute of Technology since 2012 and now is an associate professor and PhD supervisor. She chairs three NSF projects and mainly works on optimization methods, theory, and different applications.


主办教师:孔令臣



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