Multi-Objective Regular Mapping QoS Path Planning for Mega LEO Constellation Networks
Bibliographic record
Abstract
To guarantee the low-congestion performance and quality of service (QoS) requirements of multi-services in Mega Low Earth Orbit Constellation Networks (MLEOCN), this paper focuses on the comprehensive communication link model in MLEOCN, commencing from users to access satellites, relayed by relay satellites, and finally delivered to the gateway by feeder satellites. Aiming at the problems of high congestion and low throughput in traditional path planning algorithms, we innovatively propose a multi-objective optimization service-correlated path optimization algorithm based on stochastic hill climbing strategy (MSCPO-SHCS). The algorithm initially achieves the joint optimization of three metrics through regular mapping and judicious weighting. Subsequently, it assesses the interplane hop via geometric parameter theory analysis (GPTA), then decouples the large-scale mixed integer optimization problem into the integer optimization problem superimposed linear programming problem, and ultimately employs the stochastic hill climbing strategy (SHCS) for path intelligent optimization. Based on the path Gaussianity assumption, we theoretically prove and numerically verify the convergence of the proposed algorithm. The simulation results indicate that the proposed algorithm boosts the throughput and load balancing coefficient compared with the greedy strategy, service-uncorrelated, minimum hop count, and resource allocation optimization. Additionally, it decreases the hop count compared with the maximum throughput and maximum balancing coefficient and maintains the optimal overall performance.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".