A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access

TL;DR

This paper introduces a centralized multi-stage non-parametric learning algorithm for opportunistic spectrum access in cognitive radio networks, aiming to improve throughput and energy efficiency while minimizing interference with primary users.

Contribution

It proposes a novel multi-stage algorithm combining channel assignment, non-parametric traffic estimation, and adaptive collision control for spectrum sharing.

Findings

Effective channel assignment to secondary users

Accurate primary traffic distribution estimation

Collision rate maintained below threshold

Abstract

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.