AI-enabled routing in next generation networks: A survey

Deep learning (DL), a promising and exciting Artificial Intelligence (AI) tool, a potent method to add intelligence to wireless network especially 6 G and satellite networks with complex and dynamic radio situations and also enormous-scale topology. In the face of the characteristics such as heterogeneity, dynamism and time-variability that 6 G and space integrated networks naturally possess, it is difficult for ossified routing algorithms to meet the user's end-to-end OoS and QoE requirements. By analyzing various network arguments like delay, loss rate, and link signal-to-noise ratio, AI techniques have the potential to facilitate the identification of network dynamics such as congestion dots, traffic bottlenecks, and spectrum availability. This study provides a comprehensive survey of how AI algorithms are being utilized for network routing. This survey has three main contributions. Firstly, it represents elaborated tables summarizing the studies and their comparisons. Secondly, it outlines the key findings and missing aspects. Finally, it suggests six specific future research directions. The trend towards intelligence-based routing in next-gen networks has rapidly grown, especially in the last four years. However, to accomplish thorough comparisons and leverage synergies, perform valuable assessments using publicly available datasets and topologies, and execute detailed practical implementations (aligned with up-to-date standards) that can be embraced by industry, considerable effort is required. Reproducible research should be the focus of future efforts rather than new isolated ideas to ensure that these applications are implemented in practice.