Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks

TL;DR

This paper reviews AI and machine learning techniques for developing cognitive routing protocols in cognitive radio networks, emphasizing their potential to enhance network adaptability and performance.

Contribution

It provides a comprehensive tutorial and survey of AI methods applied to cognitive routing in CRNs, highlighting current applications and future research directions.

Findings

AI techniques can improve routing adaptability in CRNs

Various decision and learning methods are applicable to cognitive routing

Open research issues include inference, reasoning, and modeling challenges

Abstract

Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. Although various routing protocols, incorporating varying degrees of adaptiveness, have been proposed for CRNs, it is imperative for the long term success of CRNs that the design of cognitive routing protocols be pursued by the research community. Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design. In this paper, we provide a self-contained tutorial on various AI and machine-learning techniques that have been, or can be, used for developing cognitive routing protocols. We also survey the application of various classes of AI techniques to CRNs in general, and to the problem of routing in particular. We discuss various decision making techniques and learning techniques from AI and document their current and potential applications to the problem of routing in CRNs. We also highlight the various inference, reasoning, modeling, and learning sub tasks that a cognitive routing protocol must solve. Finally, open research issues and future directions of work are identified.