<![CDATA[The integration of terrestrial 5G/6G networks with Low Earth Orbit (LEO) satellite systems is creating a class of hybrid Satellite–Terrestrial Integrated Networks/Non-Terrestrial Networks (STIN/NTN) for which channel management in the face of highly dynamic topologies and traffic heterogeneity is becoming a key challenge. This paper examines intelligent approaches to radio resource allocation in such networks based on deep reinforcement learning (DRL) and multi-agent algorithms. A conceptual architecture for a channel management system in a hybrid network with a LEO segment is proposed, including a centralized Software-Defined Networking/Artificial Intelligence (SDN/AI) coordinator and distributed DRL agents at the satellite and ground base station levels. To analytically interpret the gains from intelligent management, an aggregated M/M/c/c teletraffic model is introduced, using the concept of an “equivalent number of channels,” which allows for linking DRL resource allocation with classical assessments of blocking probability and throughput. The obtained numerical results show that increasing the effective system capacity by 20% due to intelligent inter-segment load redistribution leads to a 30–40% reduction in blocking and a 10–18% increase in throughput in the high-load region, which is consistent with the results of detailed DRL studies for LEO networks presented in the modern literature.]]>
Copyrights © 2026
