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Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorXiao, Yu-Hang
dc.contributor.authorHuang, Lei
dc.contributor.authorRamírez García, David
dc.contributor.authorQian, Cheng
dc.contributor.authorSo, Hing Cheung
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.date.accessioned2023-11-27T12:20:36Z
dc.date.available2023-11-27T12:20:36Z
dc.date.issued2023-11-09
dc.description.abstractCovariance matrix recovery is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this article, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to adopt a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to increase the estimation accuracy. Simulation results, including those of the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.en
dc.description.sponsorshipThe work of Yu-Hang Xiao was supported in part by the National Natural Science Foundation of China under Grant 62201359. The work of Lei Huang was supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925108, and in part by the National Natural Science Foundation of China under Grant U1913221. The work of David Ramírez was supported in part by MCIN/AEI/10.13039/501100011033/FEDER, UE, under Grant PID2021-123182OB-I00 (EPiCENTER), and in part by the Office of Naval Research (ONR) Global under Contract N62909-23-1-2002.en
dc.description.statusPublicadoes
dc.format.extent16
dc.identifier.bibliographicCitationIEEE Transactions on Signal Processing, (2023), v. 71, pp.: 4060-4076.en
dc.identifier.doihttps://doi.org/10.1109/TSP.2023.3325664
dc.identifier.issn1053-587X
dc.identifier.publicationfirstpage4060
dc.identifier.publicationlastpage4076
dc.identifier.publicationtitleIEEE TRANSACTIONS ON SIGNAL PROCESSINGen
dc.identifier.publicationvolume71
dc.identifier.urihttps://hdl.handle.net/10016/38958
dc.identifier.uxxiAR/0000033419
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. PID2021-123182OB-I00/EPiCENTERes
dc.rights© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherCovariance matrix estimationen
dc.subject.otherMean squared error analysisen
dc.subject.otherNon-zero thresholden
dc.subject.otherOne-bit samplingen
dc.titleCovariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysisen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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