Attention-Based Spatiotemporal Model for RTT Prediction in LEO Satellite Networks
Bibliographic record
Abstract
Low Earth Orbit satellite network (LSN) is considered a key component of next-generation communication, offering wide coverage. However, their high-speed orbital motion leads to frequent handovers and delay variations, impacting real-time application performance. Round-Trip Time (RTT) serves as a crucial network performance indicator and RTT information is important for network optimization. This paper proposes an attention-based spatiotempora model (ASTM) for RTT prediction in LSNs. Leveraging the periodic handover, we designed a twolayer Transformer module that focuses on temporal patterns within individual handover cycles and long-term trends across multiple cycles, embedding handover information into position encoding, enabling the model to capture the impact of handover. In addition, as satellite trajectories significantly influence delay variations, we apply the Dynamic Graph Convolutional Network (DGCN) to analyze the satellite trajectory graph sequence, integrating features using multi-head attention mechanism for final RTT prediction. Extensive experiments show that ASTM outperforms common models, particularly in long-term predictions, with around 65 % improvement over Transformer. Furthermore, a congestion control case study showed that ASTM improved Multipath Mobility-aware QUIC (MM-QUIC) throughput by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 7. 3 8 \%}$</tex>, demonstrating its benefits in practical LSN applications.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".