Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
Efficient numerical methods in non-uniform sampling theory
Numerische Mathematik
Coping with irregular spatio-temporal sampling in sensor networks
ACM SIGCOMM Computer Communication Review
Image Authentication and Restoration Using Irregular Sampling for Traffic Enforcement Applications
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Random matrix theory and wireless communications
Communications and Information Theory
The impact of quasi-equally spaced sensor layouts on field reconstruction
Proceedings of the 6th international conference on Information processing in sensor networks
Performance of Linear Field Reconstruction Techniques With Noise and Uncertain Sensor Locations
IEEE Transactions on Signal Processing - Part I
Reconstruction of Multidimensional Signals From Irregular Noisy Samples
IEEE Transactions on Signal Processing
A subspace algorithm for certain blind identification problems
IEEE Transactions on Information Theory
Perturbation of Regular Sampling in Shift-Invariant Spaces for Frames
IEEE Transactions on Information Theory
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We 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 Marčenko-Pastur law as d → ∞. 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.