Online Traffic Prediction for LEO Constellation Networks Using Adjacent Satellite Correlation and Neural Networks

The escalating demand for global connectivity has driven Low Earth Orbit (LEO) constellation networks to become indispensable components of next-generation communication systems. As these networks evolve to support massive device connections, they generate unprecedented volumes of access network traffic requests, making accurate access network traffic prediction critically important. However, the online prediction of traffic presents significant challenges. These challenges stem from the lack of real-time correlation information, limited satellite resources, and the influence of sudden traffic changes. Due to the regular layout and predictable movement of the LEO constellation, the traffic of adjacent satellites within the same orbit plane has similar trends and strong correlations. Furthermore, the real-time traffic information can be shared between adjacent satellites via Inter-Satellite Links (ISL). Inspired by the above, this paper, for the first time, proposes an Adjacent Satellite Correlation-based Single-Hop Prediction (ASC-SHP) method for LEO constellation network online traffic prediction. This method explores the traffic correlations between adjacent satellites and employs neural networks to extract and renew the spatiotemporal deviation features of traffic between adjacent satellites autonomously. Simulation results validate that the proposed ASC SHP method reduces prediction errors by over 82% on average compared to baseline methods, with performance improving as the constellation size increases. Further simulations show that ASC-SHP remains effective under ISL instability and satellite failure conditions, while reducing computational complexity by over 80% compared to baseline methods.