GT-PAR: Graph Transformer-Aided Traffic Prediction and Adaptive Routing for Dynamic LEO Satellite Networks

Abstract

Low Earth Orbit (LEO) satellites are essential for 6G non-terrestrial networks due to their global coverage and low-latency communication. However, the highly dynamic topology and uneven traffic distribution cause routing inefficiencies. This letter proposes a Graph Transformer–aided Traffic Prediction and Adaptive Routing (GT-PAR) scheme to capture topology-dependent spatial coupling and long-range link-utilization dynamics. The ground segment periodically broadcasts lightweight link-utilization predictions, and the satellites select the next routing hops using the congestion-aware cost analyzed in Lemmas 1 and 2. The simulation results show that GT-PAR can significantly reduce the packet loss and end-to-end delay when compared with representative routing schemes.