Abstract:
With the advent of 5G and facing future 6G, various networks tend to be linked together to form an integrated heterogeneous network (Inte-HetNets). Inte-HetNets bring new...Show MoreMetadata
Abstract:
With the advent of 5G and facing future 6G, various networks tend to be linked together to form an integrated heterogeneous network (Inte-HetNets). Inte-HetNets bring new challenges to routing due to the need of crossing multiple network domains. Traditional routing methods are formidable to effectively support routing in Inte-HetNets. Machine learning is regarded as an promising technology to achieve such a goal, which has attracted efforts of many researchers. However, the literature still lacks a review on current research advance. In this paper, we review existing intelligent routing schemes based on machine learning in Inte-HetNets. We first introduce mainstream machine learning methods applied into routing. Then, we provide a taxonomy of learning-empowered routing schemes in Inte-HetNets by classifying them into three types based on routing scenarios: routing in ad hoc networks, routing in fixed backbone networks, and routing across network domains. Subsequently, we propose a set of requirements on learning-empowered routing in Inte-HetNets and employ these requirements to review the current literature. Finally, we explore several open issues based on our review and indicate future research directions of intelligent routing in Inte-HetNets.
Published in: IEEE Network ( Volume: 38, Issue: 1, January 2024)