Mobility-Aware Congestion Control for Multipath QUIC in Integrated Terrestrial Satellite Networks
Bibliographic record
Abstract
The Integrated Terrestrial and LEO Satellite Network (ITSN) has a high bandwidth-delay product (BDP) and high-speed movement, which makes congestion control difficult. We develop aMobility-AwareCOngestion control (MACO) algorithm for multipath QUIC (MPQUIC) in ITSN. MACO models the dynamic interactions between MPQUIC subflows and LEO networks, including handovers and outages triggered by satellite movement, and changes in network topology and link conditions. With the knowledge of network dynamics influenced by mobility, MACO can estimate changes in path BDP without solely relying on lengthy network probing. It employs a quick start (QS) and an effective congestion avoidance (CA) mechanism based on a multipath fluid model. The QS sets an appropriate initial cwnd to shorten the slow start duration. The CA applies a square root function to quickly increase the cwnd to the equilibrium and conservatively increase when approaching the BDP. We conduct a series of experiments to evaluate MACO using network simulator 3 (ns-3) based on collected data traces on Starlink. Simulation results demonstrate that MACO can achieve upto three times higher throughput and improve the convergence performance by 70.67% against benchmark algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".