# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 2 Related Work
### Hybrid Terrestrial-Satellite Routing Schemes  
To transmit data via terrestrial links and satellite links, the Broadband Satellite Multimedia (BSM) system based on gateway is proposed [13]; the satellites are taken as a relay node of the terrestrial network through gateways. To reduce link failures or congestion, a Hybrid Network Management (HNM) method based on smart antenna [14] is proposed, in which antenna arrays at the backhaul nodes are deployed to enable topology reconfiguration through ground station-satellite links. However, limited by the deployment of ground stations, the communication capabilities of TSINs are greatly restricted. To realize unified routing management of TSINs, several extended terrestrial routing protocols are adopted and modified for the satellite networks, such as the modified Open Shortest Path First (OSPF) [17] and Border Gateway Protocol (BGP) [15], in which state information of all inter-satellite links is collected to calculate routing table periodically, which will result in significant computational and signaling complexity for high dynamic satellite networks. In recent years, software defined network (SDN)-based routing algorithms have been proposed to centrally manage network routing [27; 28; 29], then greedy algorithm is used to obtain the global optimal path based on the real-time link state. However, the time-varying topology of the network will bring frequent path updates and increase the computational overhead. Graph theory has been proposed and drawn a significant amount of research interests in predictable dynamic networks [30; 31; 32; 33; 34; 35; 36; 37; 38]. [PERSON] et al. proposed graph-based resource allocation for integrated space and terrestrial communications in [30], where the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks are described via a hypergraph and its attributes. To model the network topology, graph-based deep learning has achieved good performance in a series of problems in communication networks. [PERSON] [31] reviews the research using different graph-based deep learning models, e.g., graph convolutional and graph attention networks, in various problems from different types of communication networks. The applicability of the graph-based deep learning in the construction of efficient routing protocols in realistic scenarios is analyzed in [32; 33]. In [32], by exploiting the graph neural network's topology extraction capabilities, a graph neural network-based learning approach for routing in a satellite network is proposed. However, learning-based dynamic routing approaches usually have poor generalization ability for different topologies and incomplete state information, which will degrade their performance in a dynamic environment. To handle the time-varying topology, frequent link handover and imbalanced communication load, GRouting algorithm combining graph neural networks with deep reinforcement learning is proposed to dynamically find the optimal routing paths for the LEO satellite. To achieve efficient observation data transmission, recent works [34; 35; 36] design a communication link planning algorithm according to the network topology information. For example, according to the greedy forwarding strategy, [PERSON] et al. [34] proposed a hyperbolic geometry-based routing strategy to decrease storage overhead. Furthermore, a service-aware communication link planning algorithm [37; 38] is proposed to improve network performance. However, a careful study with the consideration of the frequent changes of inter-satellite link and TSE-satellite link selection is missing. Table 1 summarizes the different routing algorithm features. The routing algorithms based on virtual topology and node simplify the mobility of satellite networks in a periodic sequence. To avoid link congestion, load balancing routing algorithms update the routing path according to the congestion state step-by-step. The relay routing and extended  
terrestrial routing mainly focus on data forwarding around the ground station.