AI-Enabled Hierarchical Network Selection in Integrated 6G TN-NTN Architectures

Sixth-generation (6G) wireless systems target ubiquitous connectivity by integrating terrestrial network (TNs) and non-terrestrial network (NTN) cooperation, including low-Earth-orbit (LEO) satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles (UAVs). In such a vertically stratified architecture characterized by massive multi-connectivity, the selection of the most suitable tier or tier-combination for each mobile user is a complicated task, as the decision depends jointly on the propagation environment, user requirements, and the instantaneous characteristics of the topology. This paper proposes an altitude-aware Deep Learning (DL) framework that casts multi-tier network selection as a seven-class classification problem spanning standalone TN, LEO, HAPS, and UAV access as well as their TN-assisted multi-connectivity combinations, as the three NTN tiers differ by up to four orders of magnitude in altitude and are therefore not interchangeable from a latency and throughput perspective. A fifteen-feature dataset is generated through extensive Monte Carlo simulations based on standardized channel and geometry models, including 3GPP TR 38.901 terrestrial pathloss and line-of-sight probability, satellite-constellation elevation angles, and air-to-ground link geometry for HAPS and UAV. A deep neural network (DNN) is trained on the resulting dataset and assessed using stratified five-fold cross-validation. The proposed model achieves an overall classification accuracy of 92.8% with a macro-averaged F1-score of 0.87 and a single-sample inference latency of approximately 1.8 ms, well within typical 6G handover budgets. The results demonstrate that requirement- and geometry-aware tier selection can be performed accurately, enabling real-time decision-making in ambiguous multi-connectivity configurations for 6G networks.