An Adaptive Rule-based Path Selection Method using Link Information in Non-Terrestrial Networks

Non-terrestrial networks (NTN) are becoming an attractive approach in beyond 5G/6G era to provide ubiquitous connectivity to everywhere, including uncovered or underserved areas. NTN enables efficient coverage of large areas from the sky by using satellites and aircraft as flying network nodes such as base stations and routers. In contrast, the node mobility of NTN introduces dynamic changes in the network topology, requiring continuous updates of the control plane, such as routing, to ensure stable communications in terms of packet delivery and latency. In addition, dynamic changes in the communication environment, such as weather, cause link quality and availability to fluctuate. As a result, existing path selection approaches may result in the selection of poor-quality paths even though they involve frequent control message flooding for topology discovery and path finding. This paper proposes a centralized path selection method in NTN using multiple path selection rules adaptively based on link information to increase packet delivery rate with fewer control messages. The path selection rule at each time is predicted by a machine learning (ML) model based on the link information. The rule prediction model is trained by training scenarios of an NTN with different parameters. Simulation results show that the proposed method outperforms the existing methods in terms of packet delivery rate and maximum latency with less than 1 % of control messages. The results also show that each of the multiple path selection rules in the proposed method contributes to increasing the packet delivery rate.