AN EFFICIENT QUADRATIC PROGRAMMING RELAXATION BASED ALGORITHM FOR LARGE-SCALE MIMO DETECTION

doi:10.1137/20M1346912

CSpace

	AN EFFICIENT QUADRATIC PROGRAMMING RELAXATION BASED ALGORITHM FOR LARGE-SCALE MIMO DETECTION
	Zhao, Ping-Fan 1; Li, Qing-Na 2; Chen, Wei-Kun 2; Liu, Ya-Feng 3
	2021
发表期刊	SIAM JOURNAL ON OPTIMIZATION
ISSN	1052-6234
卷号	31 期号:2 页码:1519-1545
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 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 quadratic programming (QP) relaxation based algorithm for solving the large-scale MIMO detection problem. In particular, we first reformulate the MIMO detection problem as a sparse QP problem. By dropping the sparse constraint, the resulting relaxation problem shares the same global minimizer with the sparse QP 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 vector of transmitted 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 than a recently proposed generalized power method.
关键词	MIMO detection projected Newton method quadratic penalty method semidefinite relaxation sparse quadratic programming relaxation
DOI	10.1137/20M1346912
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[12071032] ; National Natural Science Foundation of China (NSFC)[11671036] ; NSFC[12022116] ; NSFC[12021001] ; NSFC[11688101] ; NSFC[11631013] ; Beijing Institute of Technology Research Fund Program for Young Scholars
WOS研究方向	Mathematics
WOS类目	Mathematics, Applied
WOS记录号	WOS:000674142800017
出版者	SIAM PUBLICATIONS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59039
专题	中国科学院数学与系统科学研究院
通讯作者	Li, Qing-Na
作者单位	1.Beijing Inst Technol, Sch Math & Stat, Beijing, Peoples R China 2.Beijing Inst Technol, Beijing Key Lab MCAACI, Sch Math & Stat, Beijing, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Computat Math & Sci Engn Comp, State Key Lab Sci & Engn Comp, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Ping-Fan,Li, Qing-Na,Chen, Wei-Kun,et al. AN EFFICIENT QUADRATIC PROGRAMMING RELAXATION BASED ALGORITHM FOR LARGE-SCALE MIMO DETECTION[J]. SIAM JOURNAL ON OPTIMIZATION,2021,31(2):1519-1545.
APA	Zhao, Ping-Fan,Li, Qing-Na,Chen, Wei-Kun,&Liu, Ya-Feng.(2021).AN EFFICIENT QUADRATIC PROGRAMMING RELAXATION BASED ALGORITHM FOR LARGE-SCALE MIMO DETECTION.SIAM JOURNAL ON OPTIMIZATION,31(2),1519-1545.
MLA	Zhao, Ping-Fan,et al."AN EFFICIENT QUADRATIC PROGRAMMING RELAXATION BASED ALGORITHM FOR LARGE-SCALE MIMO DETECTION".SIAM JOURNAL ON OPTIMIZATION 31.2(2021):1519-1545.