Blind Demixing for Low-Latency Communication

doi:10.1109/TWC.2018.2886191

	Blind Demixing for Low-Latency Communication
	Dong, Jialin; Yang, Kai; Shi, Yuanming
	2019-02
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (IF:8.9[JCR-2023],8.6[5-Year])
ISSN	1536-1276
EISSN	1558-2248
卷号	18 期号:2 页码:897-911
发表状态	已发表
DOI	10.1109/TWC.2018.2886191
摘要	In next-generation wireless networks, low-latency communication is critical to support emerging diversified applications, e.g., tactile Internet and virtual reality. In this paper, a novel blind demixing approach is developed to reduce the channel signaling overhead, thereby supporting low-latency communication. Specifically, we develop a low-rank approach to recover the original information only based on the single observed vector without any channel estimation. To address the unique challenges of multiple non-convex rank-one constraints, the quotient manifold geometry of the product of complex symmetric rank-one matrices is exploited. This is achieved by equivalently reformulating the original problem that uses complex asymmetric matrices to the one that uses Hermitian positive semidefinite matrices. We further generalize the geometric concepts of the complex product manifold via element-wise extension of the geometric concepts of the individual manifolds. The scalable Riemannian optimization algorithms, i.e., the Riemannian gradient descent algorithm and the Riemannian trust-region algorithm, are then developed to solve the blind demixing problem efficiently with low iteration complexity and low iteration cost. The statistical analysis shows that the Riemannian gradient descent with spectral initialization is guaranteed to linearly converge to the ground truth signals provided sufficient measurements. In addition, the Riemannian trust-region algorithm is provable to converge to an approximate local minimum from the arbitrary initialization point. Numerical experiments have been carried out in settings with different types of encoding matrices to demonstrate the algorithmic advantages, performance gains, and sample efficiency of the Riemannian optimization algorithms.
关键词	Blind demixing low-latency communication low-rank optimization product manifold Riemannian optimization Blind Demixing, Low-latency Communication, Low-rank Optimization, Product Manifold, Riemannian Optimization.
URL	查看原文
收录类别	SCIE ; EI ; SCI
语种	英语
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000458842600012
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/243311
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_石远明组信息科学与技术学院_博士生
通讯作者	Shi, Yuanming
作者单位	ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	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	Dong, Jialin,Yang, Kai,Shi, Yuanming. Blind Demixing for Low-Latency Communication[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2019,18(2):897-911.
APA	Dong, Jialin,Yang, Kai,&Shi, Yuanming.(2019).Blind Demixing for Low-Latency Communication.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,18(2),897-911.
MLA	Dong, Jialin,et al."Blind Demixing for Low-Latency Communication".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 18.2(2019):897-911.