  Quickest change detection (QCD) is a fundamental problem in many
applications. Given a sequence of measurements that exhibits two different
distributions around a certain flipping point, the goal is to detect the change
in distribution around the flipping point as quickly as possible. The QCD
problem appears in many practical applications, e.g., quality control, power
system line outage detection, spectrum reuse, and resource allocation and
scheduling. In this paper, we focus on spectrum sensing as our application
since it is a critical process for proper functionality of cognitive radio
networks. Relying on the cumulative sum (CUSUM), we derive the probability of
detection and the probability of false alarm of CUSUM based spectrum sensing.
We show the correctness of our derivations using numerical simulations.
