Abstract:
The spectrum low utilization and high demand conundrum created a bottleneck towards
ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated
as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting
temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became
the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen
its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial
intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application
of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the
gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies
of spectrum management. This is a prospective review whose primary objective is to provide an in-depth
exploration of the recent trends in AI strategies employed in mobile and wireless communication networks.
The existing reviews in this area have not considered the relevance of incorporating the mathematical
fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking
problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated
into spectrum management problems. Beyond reviewing different ways in which deep architectures can be
integrated into spectrum management, model selection strategies and how different deep architectures can
be tailored into the CR space to achieve better performance in complex environments are then reported in
the context of future research directions.
