Statistical Signal Processing and Random Matrix Theory

We consider in a new light the age old question of discriminating
signal and noise from finite samples. Applications in mind are as
diverse as radar, sonar, wireless communications, bio-informatics and
machine learning.
We provide an application-independent approach that brings into sharp focus
a fundamental finite-sample statistical limit of signal detection.
What emerges is yet another example of how random matrix theory is
transforming the theory and practice of statistical signal processing.
Continuing on this success, with random matrix theory as a tool, we see
opportunities for creative applications of the theory in other core areas
of electrical engineering.