Minimum Round‐Trip Time Prediction for Low Earth Orbit Satellite Networks
Bibliographic record
Abstract
ABSTRACT Low Earth orbit (LEO) satellite networks, characterized by wide coverage, low latency, and high bandwidth, will be a key component of the future 6G mobile communication networks. However, the periodic handover of satellites in LEO networks will lead to dynamic path changes and fluctuations in propagation delay, affecting the end‐to‐end performance. Establishing a minimum round‐trip time (minRTT) prediction model for LEO satellite networks is crucial for optimizing the design of related mechanisms such as routing, congestion control, and loss recovery. To this end, this paper first conducts a Starlink measurement to collect real‐life data and then proposes a novel model combining hybrid attention (HA) mechanisms with multiscale convolutional neural networks (MCNN) and long short‐term memory networks (LSTM) to explore minRTT prediction. The method utilizes HA to highlight important positional features in the minRTT sequence, while the MCNN and LSTM are exploited to capture the variation patterns of minRTTs, thereby enhancing the prediction accuracy. Experimental results show that the proposed HA‐MCNN‐LSTM model outperforms existing methods.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".