Deep Reinforcement Learning-Based Joint Optimization of Routing and Wavelength Assignment for Satellite Optical Networks

Low earth orbit (LEO) satellite networks are becoming increasingly important for the development of sixth-generation mobile communication systems along with the concept of integrated space-air-ground networks. However, LEO satellite networks face numerous challenges due to their dynamic topology, large number of satellite nodes, unbalanced load distribution, and differences in communication capabilities. These factors make it difficult for traditional routing strategies to effectively handle the complex network environment and meet the requirements for low latency. To solve these problems, this paper proposes a deep reinforcement learning (DRL)-based routing algorithm for LEO satellite networks, aimed at minimizing the overall end-to-end delay through joint optimization of routing and wavelength resources. The algorithm combines DRL with a wavelength switching model and queuing theory to dynamically optimize routing decisions in response to the existing network state. Simulation results demonstrate that the algorithm exhibits strong convergence properties and effectively reduces full-path delay compared to traditional routing algorithms.