DocumentCode
735094
Title
A novel prewhitening subspace channel estimation for cyclic prefixed MIMO-OFDM systems
Author
Jung-Lang Yu ; Wei-Ting Hsu ; Biling Zhang
Author_Institution
Dept. of Electr. Eng., Fu Jen Catholic Univ., New Taipei City, Taiwan
fYear
2015
fDate
12-15 July 2015
Firstpage
891
Lastpage
895
Abstract
A novel noise whitening technique of blind subspace channel estimations is proposed for cyclic prefixing multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A fast convergence subspace channel estimation for cyclic prefixed MIMO-OFDM systems was presented in [1] where the repetition index was used to increase the number of equivalent received signals. However, the repetition index approach suffers from the nonwhite noise effects. In this paper, the noise correlation matrices are first calculated. Then a prewhitening technique is developed from the Choleskey decomposition of the noise correlation matrix. The noise whitening technique removes the correlation of the non-white noise. Computer Simulations show the effectiveness of the proposed noise whitening method.
Keywords
MIMO communication; OFDM modulation; channel estimation; convergence; interference suppression; matrix decomposition; blind subspace channel estimation; cyclic prefixed MIMO-OFDM system; fast convergence subspace channel estimation; multiple input multiple output orthogonal frequency division multiplexing system; noise correlation matrix Choleskey decomposition; noise whitening technique; nonwhite noise effects; nonwhite noise removal; prewhitening subspace channel estimation; repetition index approach; Channel estimation; Correlation; Indexes; Matrix decomposition; Noise; OFDM; Receiving antennas; MIMO; NMSE; OFDM; blind channel estimation; inter-symbol interference;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on
Conference_Location
Chengdu
Type
conf
DOI
10.1109/ChinaSIP.2015.7230533
Filename
7230533
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