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Universal Lower Bound for Finite-Sample Reconstruction Error and Its Relation to Prolate Spheroidal Functions

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We consider the problem of representing a finite-energy signal with a finite number of samples. When the signal is interpolated via sinc function from the samples, there will be a… Click to show full abstract

We consider the problem of representing a finite-energy signal with a finite number of samples. When the signal is interpolated via sinc function from the samples, there will be a certain reconstruction error since only a finite number of samples are used. Without making any additional assumptions, we derive a lower bound for this error. This error bound depends on the number of samples but nothing else, and is thus represented as a universal curve of error versus number of samples. Furthermore, the existence of a function that achieves the bound shows that this is the tightest such bound possible.

Keywords: error; bound; number samples; reconstruction error; lower bound

Journal Title: IEEE Signal Processing Letters
Year Published: 2018

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