Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach

doi:10.1109/TWC.2018.2797203

	Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach
	Yuanming Shi1 ; Jun Zhang 2; Wei Chen 3; Khaled B. Letaief 4
	2018-04
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
ISSN	1536-1276
卷号	17 期号:4 页码:2511-2524
发表状态	已发表
DOI	10.1109/TWC.2018.2797203
摘要	Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks, which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed l(p)-minimization approach with the iterative reweighted-l(2) algorithm to return a Karush-Kuhn-Tucker (KKT) point solution, as well as enhance the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closed-form solutions at each iteration to reduce the computational complexity. The well-structured solutions provide opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information, which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed l(p)-minimization approach, as well as the effectiveness of the large system analysis-based framework for computing the RRH ordering criterion.
关键词	Cloud-RAN green communications sparse optimization smoothed l(p)-minimization Lagrangian duality and random matrix theory Cloud-ran , Green Communications , Sparse Optimization , Smoothed Lp-minimization , Lagrangian Duality , Random Matrix Theory
URL	查看原文
收录类别	SCI ; SCIE ; CPCI
语种	英语
资助项目	Chinese National 973 Program[2013CB336600]
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000429695900028
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	RADIO ACCESS NETWORKS ; LARGE SYSTEM-ANALYSIS ; SCALE CONVEX-OPTIMIZATION ; BASE STATION COOPERATION ; HETEROGENEOUS NETWORKS ; MINIMIZATION ; DOWNLINK ; MIMO ; 5G ; DENSIFICATION
原始文献类型	Article ; Proceedings Paper
来源库	IEEE
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/20181
专题	个人在本单位外知识产出
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong 3.Department of Electronic Engineering, Tsinghua University, Beijing, China 4.Hamad Bin Khalifa University, Doha, Qatar
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Yuanming Shi,Jun Zhang,Wei Chen,et al. Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2018,17(4):2511-2524.
APA	Yuanming Shi,Jun Zhang,Wei Chen,&Khaled B. Letaief.(2018).Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,17(4),2511-2524.
MLA	Yuanming Shi,et al."Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 17.4(2018):2511-2524.