Stochastic Geometry and Random Matrix Theory in Compressed Sensing

Stochastic geometry and random matrix theory can be used to model and analyse the efficacy of a new paradigm in signal processing, compressed sensing. This new perspective shows that if a signal is sufficiently simple, it can be acquired at a rate proportional to its information content rather than the worst case rate dictated by a larger ambient space containing it. These lectures will show how this phenomenon can be modelled using stochastic geometry, and will also show how standard eigen-analysis in random matrix theory can give similar results.