Oversampling and Donoho-Logan type theorems in model spaces

The aim of this paper is to extend two results from the Paley–Wiener setting to more generalmodel spaces. The first one is an analogue of the oversampling Shannon sampling formula. The second one is a version of the Donoho–Logan Large Sieve Theorem which is a quantitative estimate of the embedding of the Paley–Wiener space into an $L^2(\mathbb{R},\mu )$ space.