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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/12999

Title: Performance of an ℓ1 Regularized Subspace-based MIMO Channel Estimation with Random Sequences
Authors: Takano, Yasuhiro
Juntti, Markku
Matsumoto, Tad
Keywords: Subspace-based channel estimation
noise whitening
massive MIMO
pilot contamination
compressive sensing
Issue Date: 2015-12-04
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE Wireless Communications Letters
Volume: 5
Number: 1
Start page: 112
End page: 115
DOI: 10.1109/LWC.2015.2505727
Abstract: The conventional ℓ2 multi-burst (MB) channel estimation can achieve the Cramer-Rao bound asymptotically by using the subspace projection. However, the ℓ2 MB technique suffers from the noise enhancement problem if the training sequences (TSs) are not ideally uncorrelated. We clarify that the problem is caused by an inaccurate noise whitening process. The ℓ1 regularized MB channel estimation can, however, improve the problem by a channel impulse response length constraint. Asymptotic performance analysis shows that the ℓ1 MB can improve channel estimation performance significantly over the ℓ2 MB technique in a massive multiple-input multiple-output system when the TSs are not long enough and not ideally uncorrelated.
Rights: This is the author's version of the work. Copyright (C) 2015 IEEE. IEEE Wireless Communications Letters, 5(1), 2015, pp.112-115. DOI:10.1109/LWC.2015.2505727. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/12999
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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