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dc.contributor.authorTseng, Chih-Chunen_US
dc.contributor.authorWu, Jwo-Yuhen_US
dc.contributor.authorLee, Ta-Sungen_US
dc.date.accessioned2017-04-21T06:56:45Z-
dc.date.available2017-04-21T06:56:45Z-
dc.date.issued2016-03en_US
dc.identifier.issn0090-6778en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TCOMM.2016.2520945en_US
dc.identifier.urihttp://hdl.handle.net/11536/133499-
dc.description.abstractThis paper proposes a new compressive sensing-based downlink channel state information (CSI) estimation scheme for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The proposed scheme, which involves two-stage weighted block l(1)-minimization, exploits the block sparse nature of the angular domain representation of the MIMO channel matrices and the existence of common scattering paths in the realistic propagation environment. In the first stage of the implemented scheme, a conventional block l(1)-minimization program is solved to extract the information about the common/individual supports of the multiuser channel matrices. In the second stage, a weighted block l(1)-minimization algorithm, the weighting coefficients of which are suitably chosen to exploit the acquired support information, is used to estimate the channel matrices. The analytic performance guarantees of the proposed scheme are specified based on the block restricted isometry property of the sensing matrix. Specifically, the upper bounds of the l(2)-norm reconstruction error are derived using various assumptions regarding the weighting. The obtained analytical results enable a discussion of the selection of weighting coefficients to enhance the CSI estimation performance, and the determination of a sufficient condition under which the proposed scheme outperforms the unweighted naive solution. Computer simulations show that the proposed method achieves higher estimation accuracy as compared to an existing greedy-based algorithm.en_US
dc.language.isoen_USen_US
dc.subjectBlock sparseen_US
dc.subjectchannel estimationen_US
dc.subjectcompressive sensingen_US
dc.subjectmassive MIMOen_US
dc.subjectrestricted isometry propertyen_US
dc.subjectsparseen_US
dc.titleEnhanced Compressive Downlink CSI Recovery for FDD Massive MIMO Systems Using Weighted Block l(1)-Minimizationen_US
dc.identifier.doi10.1109/TCOMM.2016.2520945en_US
dc.identifier.journalIEEE TRANSACTIONS ON COMMUNICATIONSen_US
dc.citation.volume64en_US
dc.citation.issue3en_US
dc.citation.spage1055en_US
dc.citation.epage1067en_US
dc.contributor.department電機學院zh_TW
dc.contributor.departmentCollege of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000372844900013en_US
Appears in Collections:Articles