On-Demand Routing in LEO Mega-Constellations With Dynamic Laser Inter-Satellite Links
Bibliographic record
Abstract
Low-Earth-orbit (LEO) satellite mega-constellations are beginning to include laser inter-satellite links (LISLs) to extend the Internet to the most remote locations on Earth. Since the process of establishing these links incurs a setup delay on the order of seconds, a static network topology is generally established well in advance, which is then used for the routing calculations. However, this involves keeping links active even when they are not being used to forward traffic, leading to a poor energy efficiency. Motivated by technological advances that are gradually decreasing the LISL setup delays, we foresee scenarios in which it will be possible to compute routes and establish dynamic LISLs on demand. This will require considering setup delays as penalties that will affect the end-to-end latency. In this article, we present a nonlinear optimization model that considers these penalties in the cost function and propose three heuristic algorithms that solve the problem in a tractable way. The algorithms establish different tradeoffs in terms of performance and computational complexity. We extensively analyze metrics including average latency, route change rate, outage probability, and jitter, in Starlink's Phase I version 2 constellation. The results show the benefit of adaptive routing schemes according to the link setup delay. In particular, more complex schemes are able to decrease the average end-to-end latency, in exchange for an increase in the execution time. On the other hand, depending on the maximum values of tolerated latency, it is possible to use less computationally complex schemes, which will be more scalable for the satellite mega-constellations of the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".