Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks
Bibliographic record
Abstract
In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and storage capacity, and uneven distribution of ground traffic. Such features may cause problems such as high transmission delay, network congestion and link interruption. Establishing a stable, efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay, load balancing, and network throughput. Taking advantage of the regularity of network topology, a pre-coded inter-satellite routing algorithm with load balancing is proposed, which includes 3 parts: (a) the routing sequence coding method and the concept of gateway satellite Service Region (GSSR) are proposed; (b) the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution; (c) aiming at the uneven distribution of traffic, the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite links. Simulation results show that, for the Starlink Group-4 constellation, the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".