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

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.

Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications

“…These aspects represent the categories in which the literature is summarized, and for each of them, general solutions and relevant challenges are outlined. Furthermore, a general review of machine learning (ML)-based algorithms for CR networks is made in [ 8 ]. It is focused on various ML methods for spectrum sensing, dynamic spectrum allocation, and spectrum prediction, and the relevant research challenges are outlined.…”

“…Due to the various nodes deployment densities in different networks, the RA procedures differ in requirements and complexity. It is notable that the RA functionality is usually applied for one (or a combination) of the following three wireless network scenarios (they are also considered in recent, more general surveys in the field [ 4 , 5 , 6 , 7 , 8 ], which review machine learning and heuristic methods for RA) within the context of 5G and beyond: Homogeneous and heterogeneous networks (Het-Nets). Most current wireless communication systems are of this type.…”