HOSVD based multidimensional parameter estimation for massive MIMO system from incomplete channel measurements

The dominant multipath components for massive multiple-input multiple-output systems can be described using geometry-based channel models with R -dimensional ( R -D) parameters. These parameters are crucial for channel correlation acquisition, which is a prerequisite for many technical challenges. I...

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Published in:Multidimensional systems and signal processing 2018-10, Vol.29 (4), p.1255-1267
Main Authors: Wen, Fuxi, Xu, Yangyang
Format: Article
Language:English
Subjects:
Artificial Intelligence
Circuits and Systems
Electrical Engineering
Engineering
Iterative algorithms
Iterative methods
Mathematical models
MIMO (control systems)
Orthogonality
Parameter estimation
Signal,Image and Speech Processing
Singular value decomposition
Subspace methods
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description The dominant multipath components for massive multiple-input multiple-output systems can be described using geometry-based channel models with R -dimensional ( R -D) parameters. These parameters are crucial for channel correlation acquisition, which is a prerequisite for many technical challenges. In this paper, we consider higher-order singular value decomposition based R -D channel modeling parameter estimation from incomplete measurements. Incomplete higher-order orthogonality iteration algorithm can be utilized to solve the problem, which simultaneously achieves tensor recovery and tensor decomposition. After obtaining the signal or noise subspace, the parameters of interest can be estimated by using subspace methods.
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subjects Artificial Intelligence
Circuits and Systems
Electrical Engineering
Engineering
Iterative algorithms
Iterative methods
Mathematical models
MIMO (control systems)
Orthogonality
Parameter estimation
Signal,Image and Speech Processing
Singular value decomposition
Subspace methods
title HOSVD based multidimensional parameter estimation for massive MIMO system from incomplete channel measurements
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