Artificial intelligence based cognitive routing for cognitive radio networks

Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. Although various routing protocols incorporating varying degrees of adaptiveness and cognition have been proposed for CRNs, these works have mostly been limited by their system-level focus (that emphasizes optimization at the level of an individual cognitive radio system). The vision of CRNs as cognitive networks, however, requires that the research focus progresses from its current system-level fixation to the a network-wide optimization focus. This motivates the development of cognitive routing protocols envisioned as routing protocols that fully and seamlessly incorporate artificial intelligence (AI)-based techniques into their design. In this paper, we provide a self-contained exposition of various decision-theoretic and learning techniques from the field of AI and machine-learning that are relevant to the problem of cognitive routing in CRNs. Apart from providing necessary background, we present for each technique discussed in this paper their application in the context of CRNs in general and for the routing problem in particular. We also highlight challenges associated with these techniques and common pitfalls. Finally, open research issues and future directions of work are identified.