  Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio
(CR) was introduced as a technique to attain high spectral efficiency. As the
number of secondary users (SUs) connecting to the cognitive radio network is on
the rise, there is an imminent need for centralized algorithms that provide
high throughput and energy efficiency of the SUs while ensuring minimum
interference to the licensed users. In this work, we propose a multi-stage
algorithm that - 1) effectively assigns the available channel to the SUs, 2)
employs a non-parametric learning framework to estimate the primary traffic
distribution to minimize sensing, and 3) proposes an adaptive framework to
ensure that the collision to the primary user is below the specified threshold.
We provide comprehensive empirical validation of the method with other
approaches.
