Efficient estimation of graph signals with adaptive sampling

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buir.contributor.authorAhmadi, Mohammad Javad
dc.citation.epage3823en_US
dc.citation.spage3808en_US
dc.citation.volumeNumber68en_US
dc.contributor.authorAhmadi, Mohammad Javad
dc.contributor.authorArablouei, R.
dc.contributor.authorAbdolee, R.
dc.date.accessioned2021-02-18T11:30:37Z
dc.date.available2021-02-18T11:30:37Z
dc.date.issued2020
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe propose two new least mean squares (LMS)-based algorithms for adaptive estimation of graph signals that improve the convergence speed of the LMS algorithm while preserving its low computational complexity. The first algorithm, named extended least mean squares (ELMS), extends the LMS algorithm by virtue of reusing the signal vectors of previous iterations alongside the signal available at the current iteration. Utilizing the previous signal vectors accelerates the convergence of the ELMS algorithm at the expense of higher steady-state error compared to the LMS algorithm. To further improve the performance, we propose the fast ELMS (FELMS) algorithm in which the influence of the signal vectors of previous iterations is controlled by optimizing the gradient of the mean-square deviation (GMSD). The FELMS algorithm converges faster than the ELMS algorithm and has steady-state errors comparable to that of the LMS algorithm. We analyze the mean-square performance of ELMS and FELMS algorithms theoretically and derive the respective convergence conditions as well as the predicted MSD values. In addition, we present an adaptive sampling strategy in which the sampling probability of each node is changed according to the GMSD of the node. Computer simulations using both synthetic and real data validate the theoretical results and demonstrate the merits of the proposed algorithms.en_US
dc.description.provenanceSubmitted by Onur Emek (onur.emek@bilkent.edu.tr) on 2021-02-18T11:30:37Z No. of bitstreams: 1 Efficient_Estimation_of_Graph_Signals_With_Adaptive_Sampling.pdf: 3326173 bytes, checksum: 8ffd5c8988cd565fe900658caf24ffce (MD5)en
dc.description.provenanceMade available in DSpace on 2021-02-18T11:30:37Z (GMT). No. of bitstreams: 1 Efficient_Estimation_of_Graph_Signals_With_Adaptive_Sampling.pdf: 3326173 bytes, checksum: 8ffd5c8988cd565fe900658caf24ffce (MD5) Previous issue date: 2020en
dc.identifier.doi10.1109/TSP.2020.3002607en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/75455
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TSP.2020.3002607en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectAdaptive learningen_US
dc.subjectGraph signal processingen_US
dc.subjectLeast mean squaresen_US
dc.subjectMean-square analysisen_US
dc.subjectAdaptive samplingen_US
dc.titleEfficient estimation of graph signals with adaptive samplingen_US
dc.typeArticleen_US

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