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20211018 李庆娜 An Efficient Sparse Quadratic Programming Relaxation Based Algorithm for Large-Scale MIMO Detection

发布时间:2021-10-15 15:25    浏览次数:    来源:

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

报告人:李庆娜 教授 (北京理工大学)

时间:2021/10/18 (星期一) 10:00-11:00

腾讯会议:936 859 769  会议密码:1018


报告摘要: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. This is a joint work with Pingfan Zhao, Weikun Chen and Yafeng Liu.


报告人简介:湖南大学本科、博士,中科院数学与系统科学研究院博士后现任北京理工大学数学与统计学院教授博导中国运筹学会数学优化分会青年理事北京运筹学会理事。曾访问英国南安普顿大学香港中文大学等主要研究最优化理论与算法及应用主持国家自然科学基金青年、面上项目等撰写《最优化方法》、《凸分析讲义》、《凸分析讲义-共轭函数及其相关函数》等教材及专著《多维标度方法》等


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