Hlophe, Mduduzi ComfortMaharaj, Bodhaswar Tikanath Jugpershad2022-03-312022-03-312021-08Hlophe, M.C. & Maharaj, B.T. 2021, 'AI meets CRNs : a prospective review on the application of deep architectures in spectrum management', IEEE Access, vol. 9, pp. 113954-113996.2169-3536 (online)10.1109/ACCESS.2021.3104099http://hdl.handle.net/2263/84736The 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.enThis work is licensed under a Creative Commons Attribution 4.0 License.Beyond 5GDeep architecturesDeep learningDeep Q-learning networksDeep reinforcement learningEnergy efficiencyIntelligent spectrum managementMachine learningReinforcement learningFifth generation network technology (5G)Cognitive radio network (CRN)Internet of Things (IoT)Article