# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 5 Simulation and Performance Analysis
The DQN-LLRA algorithm proposed in this paper and the Q-learning-based routing algorithm both considered the impact of queue utilization, and their latency performance was between the Dijkstra algorithm and the Dijkstra-QU scheme. Obviously, the proposed algorithms had better latency performance than the Q-learning-based algorithm. Figures 13 and 14 present an analysis of the load-balancing parameters for the three routing algorithms. Figure 11: Reward as a function of the number of training streams. From Figure 13, it can be seen that in the test with 100 pairs of source-destination nodes, the maximum queue utilization rate of the Dijkstra-QU algorithm was the lowest among the four algorithms, while the maximum queue utilization rate of the Dijkstra algorithm was the highest. The maximum queue utilization rate of the proposed DQN-LLRA algorithm and the Q-learning-based routing algorithm was between the Dijkstra algorithm and the Dijkstra-QU scheme. Figure 12: Path delay of different algorithms under various test flows. Figure 13: Path maximum queue utilization rates of different algorithms under various test flows. From Figure 14, it can be seen that in the test with 100 source-destination node pairs, the average queue utilization rate of the Dijkstra-QU algorithm was the lowest among the four algorithms, while the average queue utilization rate of the Dijkstra algorithm was the highest. The average queue utilization rates of the proposed DQN-LLRA algorithm and the Q-learning-based routing algorithm were between the Dijkstra algorithm and the Dijkstra-QU scheme. Moreover, the proposed DQN-LLRA algorithm showed a lower queue utilization rate than the Q-learning-based algorithm. Thus, the DQN-LLRA algorithm proposed in this paper can be considered superior to the traditional shortest-path algorithm and the Q-learning-based algorithm in terms of load-balancing performance. According to the experimental results, the Dijkstra algorithm calculated the shortest path in the experiment, but the maximum average queue utilization and the link utilization rate for it were the highest, and as a load-balancing algorithm for satellite node processing, the power was poorer. By contrast, the Dijkstra-QU algorithm that set queue utilization as the optimization goal did better in determining the path to load, but its path delay was larger, and it also could not meet the requirements of the low delay of the satellite network. The path obtained by the DQN-LLRA algorithm in this paper not only ensured a low delay but also responded effectively to the load situation in the network, reducing the maximum and average queue utilization of the link. Compared with the Dijkstra algorithm, the proposed algorithm reduced the maximum link queue utilization by 8%, reduced the average queue utilization by 15%, and had a robust load-balancing ability. Compared with the Q-learning-based algorithm, the proposed algorithm reduced the maximum link queue utilization by 5%, reduced the average queue utilization by 13%, and had a robust load-balancing ability.