A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
This work addresses spectrum efficiency challenges in wireless communication for cognitive radio networks, presenting an incremental improvement over existing methods.
The paper tackles the problem of improving throughput and energy efficiency for secondary users in cognitive radio networks while minimizing interference to primary users, proposing a multi-stage algorithm that achieves these goals through channel assignment, non-parametric learning, and adaptive collision control.
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.