Adaptive Multi-Link Data Allocation for LEO Satellite Networks
Bibliographic record
Abstract
The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink’s network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".