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Article: Subspace Identification for DOA Estimation in Massive/Full-dimension MIMO Systems: Bad Data Mitigation and Automatic Source Enumeration

TitleSubspace Identification for DOA Estimation in Massive/Full-dimension MIMO Systems: Bad Data Mitigation and Automatic Source Enumeration
Authors
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78
Citation
IEEE Transactions on Signal Processing, 2015, v. 63 n. 22, p. 5897-5909 How to Cite?
AbstractIn this paper, the direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array (also known as full-dimension MIMO) is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. Based on the variational Bayesian framework, a novel iterative algorithm for subspace identification operating on tensor represented data is proposed with integrated features of effective bad data mitigation and automatic source enumeration. The subspace recovered from the proposed algorithm not only enables existing 2D DOA estimators to be readily applied, if the number of signal paths is less than the number of horizontal antennas and vertical antennas, the subspaces in elevation and azimuth domains can be separately estimated, from which one dimensional (1D) DOA estimators can be utilized, thus further lowering the complexity. Simulation results are presented to illustrate the excellent performance of the proposed subspace recovery method and subsequent DOA estimation in terms of accuracy and robustness.
Persistent Identifierhttp://hdl.handle.net/10722/231936
ISSN
2015 Impact Factor: 2.624
2015 SCImago Journal Rankings: 2.004

 

DC FieldValueLanguage
dc.contributor.authorCHENG, L-
dc.contributor.authorWu, YC-
dc.contributor.authorZhang, J-
dc.contributor.authorLiu, L-
dc.date.accessioned2016-09-20T05:26:29Z-
dc.date.available2016-09-20T05:26:29Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Signal Processing, 2015, v. 63 n. 22, p. 5897-5909-
dc.identifier.issn1053-587X-
dc.identifier.urihttp://hdl.handle.net/10722/231936-
dc.description.abstractIn this paper, the direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array (also known as full-dimension MIMO) is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. Based on the variational Bayesian framework, a novel iterative algorithm for subspace identification operating on tensor represented data is proposed with integrated features of effective bad data mitigation and automatic source enumeration. The subspace recovered from the proposed algorithm not only enables existing 2D DOA estimators to be readily applied, if the number of signal paths is less than the number of horizontal antennas and vertical antennas, the subspaces in elevation and azimuth domains can be separately estimated, from which one dimensional (1D) DOA estimators can be utilized, thus further lowering the complexity. Simulation results are presented to illustrate the excellent performance of the proposed subspace recovery method and subsequent DOA estimation in terms of accuracy and robustness.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78-
dc.relation.ispartofIEEE Transactions on Signal Processing-
dc.rightsIEEE Transactions on Signal Processing. Copyright © IEEE.-
dc.rights©20xx IEEE. 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. -
dc.titleSubspace Identification for DOA Estimation in Massive/Full-dimension MIMO Systems: Bad Data Mitigation and Automatic Source Enumeration-
dc.typeArticle-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.identifier.doi10.1109/TSP.2015.2458788-
dc.identifier.hkuros264924-
dc.identifier.volume63-
dc.identifier.issue22-
dc.identifier.spage5897-
dc.identifier.epage5909-
dc.publisher.placeUnited States-

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