Dynamic Time-Difference QoS Guarantee in Satellite–Terrestrial Integrated Networks: An Online Learning-Based Resource Scheduling Scheme
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".