个人信息Personal Information
副教授
博士生导师
硕士生导师
任职 : 大数据研究所副所长
性别:男
毕业院校:哈尔滨工程大学
学位:博士
所在单位:软件学院、国际信息与软件学院
学科:软件工程
办公地点:大连理工大学软件学院综合楼219
联系方式:+86-0411-62274379
电子邮箱:wanliangtian@dlut.edu.cn
Polarization Channel Estimation for Circular and Non-Circular Signals in Massive MIMO Systems
点击次数:
论文类型:期刊论文
发表时间:2019-09-01
发表刊物:IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
收录刊物:SCIE、EI
卷号:13
期号:5,SI
页面范围:1001-1016
ISSN号:1932-4553
关键字:Massive MIMO system; DOA and polarization estimation; circular and non-circular signals; multiple signal classification (MUSIC) algorithm
摘要:The polarization millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system has been deployed in next-generation wireless communication since it can provide a high-data stream and high space efficiency simultaneously. Polarization channel parameter estimation for polarized mmWave massive MIMO systems is extremely important for directional beamforming with data transmissions. In this paper, the base station (BS) equipped with a large polarization-sensitive array is considered in massive MIMO systems. The polarization channel consists of direction-of-arrival (DOA) and polarization parameters that are estimated from the coexistence of circular and non-circular signals. Based on the unconjugated covariance matrix, the initialized polarization channel estimation is achieved by multiple signal classification (MUSIC). Then, the high-accuracy polarization channel estimation for general non-circular rate signals is performed by reconstructing the corresponding noise matrix. The high-accuracy polarization channel estimation for circular signals is obtained based on covariance matrix differencing. Moreover, the dimension of parameter search is reduced based on the partial derivative of the spectrum function with respect to the non-circular phase. The high-accuracy polarization channel estimation for the maximum non-circular rate signal is finally achieved based on the initialized parameter estimation of the polarization channel. The proposed algorithm estimates different kinds of signals separately. The effect of different kinds of signals is reduced significantly, which means that the resolution probability of different kinds of signals can be dramatically improved. Numerical examples are provided to demonstrate the performance of the proposed algorithm, especially in small angular distances.