## 3 Related Work  
In recent years, many works addressing the problems of intelligent TE in TNs, NTNs, and integrated 3D networks have been published. Here, in focus are key routing algorithms and architectural frameworks that target optimisation under QoS constraints. Organisation of routing is one of the key issues regarding carrier-grade NTNs. In traditional TNs, routers calculate routing tables using a routing information database established based on network topology and link states. Routing updates require exchanging topology information across network devices. It leads to significant overhead and slow convergence [23], causing the inapplicability of traditional approaches in NTN scenarios due to limited resources and frequent topology changes. Therefore, academia and industry started to develop routing algorithms adapted to the NTN specificity [24]. A time-topology routing algorithm for 3D networks is presented in [25], which outperforms existing message-based routing methods in terms of latency and delivery rate. The method considers node mobility and contextual information (weather, propagation models, etc.) to obtain a time-based inter-node visibility and link information to construct the network topology for a certain time frame and generate routes providing stable communication. A QoS-aware routing minimising congestion in SDN-based NTN systems is proposed in [26]; the method assigns scores to network links based on selected QoS parameters (e.g., utilisation, congestion degree, delay). On that basis, it finds shortest paths using a modified [PERSON]'s algorithm. A compass time-space model for the scalable representation of mass-scale NTN systems and the QoS routing method is introduced in [27]. The algorithm yields QoS performance and reliability improvements compared to baseline methods. A cooperative data downloading routing scheme for LEO satellites, which uses ISL to schedule feeder link bandwidth resources, is proposed in [28]. The routing algorithm considers the limited ISL bandwidth and utilises linear programming for solving the route selection problem. Another popular NTN routing approach to provide resilience and stable QoS involves exploiting multi-path routing. A Multi-Path routing algorithm with Joint Optimisation of Load balancing (MPJOL) [29] proposes clustering of LEO satellites and selection of cluster head nodes (considering connectivity and load capacity) to reduce the network scale, and calculating k-shortest paths between the source and destination clusters. The method improves transmission efficiency and quality while reducing the route discovery overheads. The Multi-Path QoS Routing (MPIR) approach given in [30], which targets QoS improvements, proposes an algorithm to generate QoS routes using a genetic algorithm fueled by simulated annealing to handle population diversity and improve convergence. While increasing resilience, multi-path routing is, however, inefficient in large-scale networks [29] due to traffic multiplication. Another common dynamic routing approach is to embed the information about the routes within the packets and exploit Source Routing (SR) [31] or Segment Routing [32]. These solutions, however, increase the Data Plane (DP) traffic volume due to additional headers attached to each conveyed packet. Also, SR-based methods require continuous updates of preset paths in gateway nodes, which can result in significant operational overhead. With the recent onset of AI and Machine Learning (ML), more researchers have directed their attention towards using AI-based routing in complex environments such as Space-Air-Ground Integrated Networks (SAGINs) [33]. AI-driven routing (commonly DRL-based) typically aims to solve specific issues of conventional routing methods in dynamic topologies, i.e., convergence, QoS, and resource efficiency.