Robust and Adaptive Scheduling of Sequential Periodic Sensing for Cognitive Radios

Spectrum sensing is a crucial element of dynamic spectrum access (DSA) as it enables cognitive radios (CRs) to opportunistically access the under-utilized spectrum. Existing efforts on sensing have not adequately addressed sensing scheduling over time for better detection performance. In this work, we consider sequential periodic sensing of an in-band channel. We focus primarily on finding the appropriate sensing frequency during an SU´s active data transmission on a licensed channel. Detection schemes addressing channel state change and anomalous data are designed specifically to facilitate short-term sensing adaptation to the variations in sensed data. In addition, long-term adaptation is also considered so that the evolving sensing environment can be reflected in the sensing schedule as well. Simulation results demonstrate that our design guarantees better conformity to the spectrum access policies by significantly reducing the delay in change detection while ensuring better sensing accuracy.