On the d-dimensional Quasi-Equally Spaced Sampling

dc.creatorNordio, Alessandro
dc.creatorChiasserini, Carla-Fabiana
dc.creatorViterbo, Emanuele
dc.date2008-06-23
dc.date.accessioned2026-07-07T09:46:10Z
dc.date.available2026-07-07T09:46:10Z
dc.descriptionWe study a class of random matrices that appear in several communication and signal processing applications, and whose asymptotic eigenvalue distribution is closely related to the reconstruction error of an irregularly sampled bandlimited signal. We focus on the case where the random variables characterizing these matrices are d-dimensional vectors, independent, and quasi-equally spaced, i.e., they have an arbitrary distribution and their averages are vertices of a d-dimensional grid. Although a closed form expression of the eigenvalue distribution is still unknown, under these conditions we are able (i) to derive the distribution moments as the matrix size grows to infinity, while its aspect ratio is kept constant, and (ii) to show that the eigenvalue distribution tends to the Marcenko-Pastur law as d->infinity. These results can find application in several fields, as an example we show how they can be used for the estimation of the mean square error provided by linear reconstruction techniques.
dc.descriptionsubmitted to IEEE Transactions on Signal Processing
dc.identifierhttps://arxiv.org/abs/0806.3681
dc.identifierhttp://arxiv.org/abs/0806.3681
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/163438
dc.subjectInformation Theory
dc.titleOn the d-dimensional Quasi-Equally Spaced Sampling
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