Topology-Aware Learning for Routing In Satellite–Terrestrial Integrated Networks: A Review of Graph Neural Network and Reinforcement Learning Approaches

Reliable Session-Oriented Multi-Path Routing for LEO Satellite Networks: A Multi-Agent Learning Approach

Hybrid Graph Neural Network–Reinforcement Learning Framework for Intelligent Programmable Network Automation

Software‐Defined Networking (SDN) and Network Function Virtualisation (NFV) offer the benefit of dynamic control and flexible resources that can be managed with ease. However, the growing complexity of network topologies and heterogeneous traffic patterns, combined with high‐quality‐of‐service (QoS) demands, requires innovative routing schemes and existing AI‐based optimisation procedures. Most existing methods model network structures using Graph Neural Networks (GNNs) or implement Reinforcement Learning (RL) to route traffic adaptively, but rarely combine the two in a single, more representative framework. This paper proposes a hybrid GNN‐RL system coupled with a cloud‐based automated experimentation system to optimise topology‐aware, multi‐objective, and scalable programs in the network. Graph representations were created based on network flows of two heterogeneous datasets, namely NetBench and SDNFLow and a synchronised hybrid model. A normalised, multi‐objective reward function that includes throughput, latency, packet loss, and a congestion penalty was proposed to address the reward imbalance, a frequent issue in RL routing. The RL component is trained and evaluated within the abstracted simulated environment and policy‐derived QoS metrics are generated by routing actions. The controlled environment enables reproducible evaluation of the hybrid‐GNN framework. Experimental analysis indicates that the optimised framework is more effective at QoS than standalone RL and GNN baselines. Latency was reduced by more than 70.7 ms in the original experiments to 17.7 ms and further to 9.6 ms with reward optimisation. Packet loss dropped by 6.3% to 1.49%, and throughput remained steady at 151–231 Mbps, improving over the baseline (85–88 Mbps). The coordinated training scheme enhanced convergence rate and routing stability in dynamic traffic environments. These 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.

HybridRL-RNP: A Hybrid Reinforcement Learning Framework for Optimizing Router Nodes Placement in Wireless Mesh Networks Toward 6G

In Wireless Mesh Networks (WMNs), router node placement (RNP) is critical for achieving network-wide coverage, connectivity, and reliable communication performance. However, determining the optimal router placement is a nonlinear combinatorial problem with a vast and dynamic search space, which is strongly influenced by user distribution and traffic demand. To address this challenge, this study introduces a Hybrid Reinforcement Learning Framework, called HybridRL-RNP, designed to optimize router placement in WMNs. The proposed approach integrates the REINFORCE algorithm with a heuristic-guided placement strategy, enabling the learning agent to adaptively select router positions based on the current network coverage and the node density. A reward function is formulated using network connectivity (NC), which is the proportion of user nodes connected to at least one gateway as the primary optimization objective, while also considering coverage uniformity and router interconnectivity. Simulation experiments demonstrate that HybridRL-RNP achieves an average NC exceeding 96%, outperforming traditional heuristic-based and pure RL-based schemes. Moreover, the framework ensures stable inter-router topologies and scalable coverage performance for varying network densities. These 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.

Local Graph-Aware Hop-by-Hop Routing for Dynamic LEO Satellite Networks

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.

Deep Reinforcement Learning-Based QoS-Aware Routing Protocol for Space–Air–Ground Integrated Networks

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.

AI-driven multi-tier aerial communication networks: a review of routing, computing, handover, resource management, and optimization techniques

Multi-tier aerial communication networks (MACNs), integrating satellites, high-altitude platforms, and unmanned aerial vehicles, are emerging as a cornerstone of next-generation global connectivity. Their promise of resilient and ubiquitous coverage, however, is hindered by highly dynamic topologies, severe energy and computational constraints, environment-sensitive channels, diverse quality-of-service requirements, and limited real-world validation. Artificial intelligence (AI) has increasingly been explored as a flexible framework to address these challenges, enabling adaptive routing, distributed computing and task offloading, handover management, intelligent resource allocation, and large-scale network optimization. This survey provides a comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives. We critically evaluate representative approaches in terms of scalability, efficiency, data demands, and practical deployability, and identify emerging trends such as graph neural networks with reinforcement learning for dynamic routing, predictive learning for mobility management, and federated learning for distributed computation. Persistent challenges remain in lightweight edge intelligence, real-world testbeds, reproducible benchmarking, and simulation-to-deployment transfer. To address these issues, we offer a research roadmap emphasizing compressible and interpretable models, standardized benchmarks, realistic validation, and hybrid designs that balance adaptability with computational and energy overhead. Finally, we identify open challenges and future research directions, offering insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.