STLN-based channel estimation using superimposed training and first-order statistics

STLN-based channel estimation using superimposed training and first-order statistics

In this paper, Channel estimation using superimposed training and first-order statistics is considered. Information-induced interference matrix in channel estimation is of Toeplitz structure, which can be utilized for deconvolution of the system equation. A structured total least norm (STLN) approac...

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Hauptverfasser: Chunquan He, Gaoqi Dou, Jun Gao, Cheng Fan
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Sprache:eng
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channel estimation
least squares
structured total least squares norm
superimposed training
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Gaoqi Dou
Jun Gao
Cheng Fan
description In this paper, Channel estimation using superimposed training and first-order statistics is considered. Information-induced interference matrix in channel estimation is of Toeplitz structure, which can be utilized for deconvolution of the system equation. A structured total least norm (STLN) approach is introduced to improve the estimation performance. Simulation results show the enhancement performance of the STLN estimator when compared with the LS, total least squares (TLS) and data least squares (DLS) estimators.
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subjects channel estimation
least squares
structured total least squares norm
superimposed training
title STLN-based channel estimation using superimposed training and first-order statistics
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