Dynamic Time-Difference QoS Guarantee in Satellite–Terrestrial Integrated Networks: An Online Learning-Based Resource Scheduling Scheme

The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning. However, given the inherent volatility of network traffic, ensuring differentiated quality of service in highly dynamic networks remains a significant challenge. In this paper, we propose an online learning-based resource scheduling scheme for satellite–terrestrial integrated networks (STINs) aimed at providing on-demand services with minimal resource utilization. Specifically, we focus on: ① accurately characterizing the STIN channel, ② predicting resource demand with uncertainty guarantees, and ③ implementing mixed timescale resource scheduling. For the STIN channel, we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks. We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction, while introducing conformal prediction to mitigate uncertainties arising from burst traffic. Additionally, we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale. We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information. Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform, numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.