Abstract: With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates, satellites have become an important part of data transmission in air-ground networks. However, due to the factors such as geographical location and people’s living habits, the differences in user’ demand for multimedia data will result in unbalanced network traffic, which may lead to network congestion and affect data transmission. In addition, in traditional satellite network transmission, the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner, which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above, in this paper artificial intelligence technology is applied to the satellite network, and a software-defined network is used to obtain the global network information, perceive network traffic, develop comprehensive decisions online through reinforcement learning, and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing, the throughput is 8% higher, and the proposed method has load balancing characteristics.

Key words: Software-defined network (SDN); Quick user datagram protocol Internet connection (QUIC); Reinforcement learning; Sketch; Multi-service demand; Satellite network

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