A Survey on Machine Learning Algorithms for Applications in Cognitive Radio Networks

TL;DR

This survey reviews how machine learning algorithms are applied to address key challenges in cognitive radio networks, focusing on spectrum sensing and dynamic spectrum access, and discusses recent advancements and implementation challenges.

Contribution

It provides a comprehensive overview of ML techniques used in CRNs, highlighting recent developments and identifying challenges for real-time deployment.

Findings

ML improves spectrum sensing accuracy in CRNs

ML-based spectrum prediction enhances dynamic spectrum access

Real-time implementation of ML in CRNs faces significant challenges

Abstract

In this paper, we present a survey on the utility of machine learning (ML) algorithms for applications in cognitive radio networks (CRN). We start with a high-level overview of some of the major challenges in CRNs, and mention the ML architectures and algorithms that can be used to alleviate them. In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing -- with non-cooperative and cooperative scenarios, and dynamic spectrum access -- with spectrum auction and prediction. We present a detailed study of recent advancements in the field of ML in CRNs for these applications, and briefly discuss the set of challenges in real-time implementation of ML techniques for CRNs.