Message Passing DQN Enhanced Fault Tolerance Traffic Routing for Dynamic 6G Edge Networks

In the high-density data transmission and multi-terminal device environment of 6G edge networks, an efficient routing strategy is crucial. Existing routing methods lack adaptability in the face of network dynamics, which can result in service delays and connection interruptions. To address this issue, this paper proposes an innovative fault-tolerant traffic routing (FTTR) mechanism. Leveraging Message Passing Neural Networks (MPNNs) and Deep Q-Network (DQN), FTTR can deeply explore the interdependencies between links and make precise routing decisions. Extensive experiments demonstrate that FTTR mechanism achieves an average 27.72% increase in network load capacity compared to mainstream routing strategies. Its generalization and robustness significantly outperform Proximal Policy Optimization (PPO), clearly demonstrating its adaptability and stability to network dynamics.