Efficient numerical methods in non-uniform sampling theory
Numerische Mathematik
Multiuser Detection
Coping with irregular spatio-temporal sampling in sensor networks
ACM SIGCOMM Computer Communication Review
Random matrix theory and wireless communications
Communications and Information Theory
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
Design of reduced-rank MMSE multiuser detectors using random matrix methods
IEEE Transactions on Information Theory
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We study the performance of signal estimation and reconstruction systems, that exploit the linear minimum mean square error (LMMSE) technique. This model often occurs in signal processing and wireless communications; some examples are radar applications, MIMO communications, or sensor networks sampling a physical field. Our performance analysis implies the characterization of a random matrix product, involving a multifold Vandermonde matrix with complex exponential entries. We therefore derive the LMMSE by computing the η-transform of this matrix product, which can be evaluated either by implicit as well as by explicit expression, using the matrix asymptotic moments. Finally, we show how our results can be applied in some cases of practical interest.