# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 2 Related Work
### Load Balancing of LEO Satellites  
The inter-satellite link length of LEO satellites changes with the latitude of the satellites. The conventional shortest-path algorithm design for the routing paths only relies on the length of the path, which results in traffic aggregation when the latitude is high [1; 16]. Figure 3 depicts a 3D schematic of the distribution of satellite traffic, created using NS3 network simulation software. The black dots represent LEO satellites, while the line segments denote inter-satellite links carrying traffic. The thickness and color of each segment correspond to its bandwidth utilization and traffic, respectively. The thicker lines indicate  
Figure 2: Iridium satellite trajectory diagram.  
higher traffic, while the darker colors show greater bandwidth utilization. Notably, the LEO satellite network experiences congestion primarily in high-latitude and densely populated regions. In addition, the uneven distribution of the gateway station on the ground gives rise to the load imbalance of the satellite networks. User mobility and the global population distribution are also key factors affecting the distribution of traffic flows [17]. The high mobility of the satellites leads to rapid changes in the high-load coverage area between satellites [18; 19].  
A path-based load-balancing satellite routing algorithm with the objective of minimizing the maximum network flow was proposed in [17; 20]. This algorithm avoided traffic-flow aggregation in high-latitude areas by setting all inter-satellite links with the same path length and giving all paths the same priority. The authors in [21] divided the transmission areas into the heavy-load ranges and the light-load ranges considering the relationship between the reverse slots and the gateway stations. The congestion indicator was utilized for the heavy-load range, and the path with the minimum weight was exploited to deal with the uneven traffic-flow distribution. Nevertheless, this method required the link status information of the whole network and could not make decisions in real time [22]. The Elastic Load-Balancing (ELB) algorithm was proposed in [23; 24], which realized the exchange of congestion information between satellite nodes. Thus, the ELB achieved the load-balancing goal and avoided traffic congestion. The occupancy of the queue was leveraged to decide whether the satellite node was idle or busy. When a node was marked as a busy one, it emitted a message to its neighbor nodes to decrease its transmission rate. The TLR algorithm proposed in [25] took both the current status of the congestion and the possible status of the next hop congestion into account. The authors in [26] proposed an iterative Dijkstra mechanism to select the optimal transmission paths for load-balancing routing. The authors in [27] considered the link delay to further promote the routing performance. Cooperative game theory was exploited in [28] to bring to equilibrium the tradeoff between the load and the transmission delay when exploring the LEO routing algorithm. Fuzzy theory was utilized to achieve the requirements of various users in [29]. The transmission overhead and the routing convergence were evaluated as the key performance metrics in [30]. The on-demand dynamic routing algorithm was proposed  
Figure 3: Traffic distribution diagram.  
with the track prediction. Energy consumption was also considered in [31] to promote the service quality for the users.  
The existing literature highlights the benefits of LEO routing in terms of load performance [32,33]. However, challenges related to the insufficient local optimization and weak dynamic adaptability still remain unsolved, which could potentially impede the development of the LEO satellite network.