Adaptive Resource Provisioning in Satellite Networks via VAE-Assisted Contextual Bandit Learning
Bibliographic record
Abstract
Resource provisioning in low Earth orbit satellite networks (LSNs) is critical for managing resources from highly dynamic and time-varying satellites. In this paper, we investigate a resource provisioning problem to ensure efficient satellite resource utilization with satisfied delay performance in LSNs. To address the challenges of uncertain service demand prediction, inter-area contention, and inherent satellite mobility, we propose a variational autoencoder (VAE)-assisted contextual bandit learning-based resource provisioning scheme for adaptive resource management. Specifically, a VAE module is used to predict future service demand along with uncertainty, enhancing the robustness of provisioning decisions. To mitigate resource conflict among different areas, a resource occupancy risk estimation mechanism is designed by analyzing satellite coverage duration and area overlap. Furthermore, a contextual bandit learning framework with a set transformer-based policy network is constructed to support decision-making under time-varying numbers of available satellites. Simulation results show that the proposed scheme outperforms benchmark schemes in terms of resource usage and delay satisfaction rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".