Comparative Analysis of Spatial Covariance Matrix Estimation Methods in OFDM Communication Systems

2006 ◽  
Author(s):  
Alexander Maltsev ◽  
Roman Maslennikov ◽  
Alexey Khoryaev
Keyword(s):  
Comparative Analysis ◽  
Covariance Matrix ◽  
Communication Systems ◽  
Estimation Methods ◽  
Covariance Matrix Estimation ◽  
Spatial Covariance ◽  
Matrix Estimation

scholarly journals Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3368
Author(s):  
Rui Hu ◽  
Jun Tong ◽  
Jiangtao Xi ◽  
Qinghua Guo ◽  
Yanguang Yu
Keyword(s):  
Covariance Matrix ◽  
Communication Systems ◽  
Low Complexity ◽  
Low Rank ◽  
Covariance Matrix Estimation ◽  
Hardware Complexity ◽  
Matrix Estimation ◽  
Planar Arrays ◽  
Rank Matrix ◽  
Low Rank Matrix

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as potential candidates for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method.

scholarly journals A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Zongliang Hu ◽  
Kai Dong ◽  
Wenlin Dai ◽  
Tiejun Tong
Keyword(s):  
Covariance Matrix ◽  
Statistical Tests ◽  
Estimation Methods ◽  
Covariance Matrix Estimation ◽  
High Dimensional ◽  
Simulation Studies ◽  
Data Set ◽  
Matrix Estimation ◽  
Comparison Results ◽  
Practical Guidelines

Abstract The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

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