Topology-Aware Learning for Routing In Satellite–Terrestrial Integrated Networks: A Review of Graph Neural Network and Reinforcement Learning Approaches
2026· International journal of research and innovation in applied science· 0 citations
TL;DR
This review analyses topology-aware learning-based routing for STINs, concentrating on Graph Neural Networks (GNNs) and hybrid GNN–Reinforcement Learning (GNN–RL) frameworks.
Abstract
Satellite–Terrestrial Integrated Networks (STINs) are a key part of 5G-Advanced and new 6G non-terrestrial networks. They connect the world by combining LEO, MEO, and GEO satellite constellations with ground-based infrastructure. But routing is very hard because of highly dynamic topologies, different link characteristics, and large-scale networks. This makes traditional protocols and topology-agnostic learning methods less useful. This review analyses topology-aware learning-based routing for STINs, concentrating on Graph Neural Networks (GNNs) and hybrid GNN–Reinforcement Learning (GNN–RL) frameworks. By modelling STINs as graphs that change over time, these methods clearly show how relationships and multi-hop interactions work, which are important for routing that can grow and change. Comprehensive analyses are conducted on classical routing, non-topology-aware reinforcement learning, purely GNN-based methodologies, and hybrid GNN–RL architectures, emphasising their merits and drawbacks in dynamic satellite–terrestrial contexts. We also look at hierarchical and multi-agent extensions, as well as current datasets and evaluation methods. Finally, important open problems related to scalability, non-stationarity, and real-world use are found, and future research directions that fit with new 6G non-terrestrial network standards are laid out.
Results affirm that the combination of structural learning, adaptive decision‐making, and automated evaluation on a cloud platform offers a realisable, scalable route to intelligent, autonomous, programmable network management that can be used in next‐generation communication infrastructures.
Muhammad Hasnain, Faisal Naeem, Imran Ghani· Applied AI Letters· 0 citations
Results highlight the effectiveness and practicality of the proposed HybridRL-RNP framework as an intelligent topology control solution for Wireless Mesh Networks towards 6G, where the synergy between AI-driven optimization and heuristic knowledge plays a pivotal role in achieving globally optimal network connectivity.
Le Huu Binh, Thuy-Van T Duong, Le Duc Huy· IEEE Access· 0 citations
Low Earth orbit (LEO) satellite networks exhibit rapidly changing topology and time-varying traffic hotspots, which makes hop-by-hop routing highly sensitive to local congestion and state staleness. Existing routing methods either rely on global path computation or use plain local observations, while graph-enhanced approaches often focus on generic neighborhood representation rather than direct comparison among candidate next hops. To address this issue, this paper proposes a Local Graph-Aware Routing method (LGAR) for dynamic LEO satellite networks. LGAR organizes the current node, reachable candidate neighbors, and candidate links into a local graph, and then constructs structured action representations through node encoding, relation message extraction, and attention-based context aggregation. The resulting representations are integrated into an off-policy actor-critic framework to support adaptive hop-by-hop routing decisions. Experiments under the hub-inversion setting show that LGAR achieves an average total delay of 47.64 ms and an average queueing delay of 5.81 ms while maintaining a delivery rate of 99.93%. Compared with MATMR, LGAR-NoGraph, and GRLR, LGAR reduces the average total delay by 12.38%, 12.85%, and 30.75%, respectively. Additional scenario, ablation, and scalability results further show that LGAR generalizes beyond the main setting and that its gain mainly comes from local graph modeling and relation-aware action encoding.
Wen-Xiang Zhang, Yiao Gao, Ke-Yan Bai et al.· 2026 8th International Confe...· 0 citations
A deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN and indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization.
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations