Irregular Array Manifold Aided Channel Estimation in Massive MIMO Communications

被引:22
作者
Cheng, Lei [1 ]
Xing, Chengwen [2 ]
Wu, Yik-Chung [3 ]
机构
[1] Shenzhen Res Inst Big Data, Shenzhen 518000, Guangdong, Peoples R China
[2] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[3] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Angle aided channel estimation; irregular three dimensional array; massive MIMO; array manifold; FD-MIMO; DESIGN; MUSIC;
D O I
10.1109/JSTSP.2019.2937392
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Angle information aided channel estimation has recently attracted extensive attention since its exploitation of the underlying angular channel structure has brought significant performance improvement. Nevertheless, current research works are mostly tailored to one dimensional (ID) or two dimensional (2D) antenna arrays. Being heavily dependent on the shift-invariant structure of the arrays, these existing results cannot be easily extended to irregular three dimensional (3D) arrays, which however are indispensable for base stations with large numbers of antennas but limited space. To extend the angle aided channel estimation to the regime of 3D irregular arrays, the channel estimation problem for an irregularly shaped array is linked to an array manifold constrained complex-valued tensor decomposition problem under missing data, and an efficient two-stage channel estimation algorithm that can automatically estimate the channel path number is further proposed. Simulation results are presented to demonstrate the excellent performance of the proposed algorithm in channel estimation, including path number enumeration.
引用
收藏
页码:974 / 988
页数:15
相关论文
共 38 条
[1]  
Amani N, 2017, PROCEEDINGS OF THE 2017 IEEE-APS TOPICAL CONFERENCE ON ANTENNAS AND PROPAGATION IN WIRELESS COMMUNICATIONS (APWC), P264, DOI 10.1109/APWC.2017.8062297
[2]  
[Anonymous], 2005, FUNDAMENTALS WIRELES
[3]  
[Anonymous], 1999, NONLINEAR PROGRAMMIN
[4]  
[Anonymous], 1993, ESIMATION THEORY
[5]  
[Anonymous], 2012, MACHINE LEARNING PRO
[6]   Massive MIMO: Ten Myths and One Critical Question [J].
Bjornson, Emil ;
Larsson, Erik G. ;
Marzetta, Thomas L. .
IEEE COMMUNICATIONS MAGAZINE, 2016, 54 (02) :114-123
[7]   Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors [J].
Cheng, Lei ;
Wu, Yik-Chung ;
Poor, H. Vincent .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (03) :663-676
[8]   Subspace Identification for DOA Estimation in Massive/Full-Dimension MIMO Systems: Bad Data Mitigation and Automatic Source Enumeration [J].
Cheng, Lei ;
Wu, Yik-Chung ;
Zhang, Jianzhong ;
Liu, Lingjia .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (22) :5897-5909
[9]   Spatially Sparse Precoding in Millimeter Wave MIMO Systems [J].
El Ayach, Omar ;
Rajagopal, Sridhar ;
Abu-Surra, Shadi ;
Pi, Zhouyue ;
Heath, Robert W., Jr. .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2014, 13 (03) :1499-1513
[10]  
Fan D., IEEE T WIRELESS COMM