STEBS: Spatio-Temporal Entropy-Based Scoring Handover Model for LEO Satellite
Bibliographic record
Abstract
In 6G and beyond network infrastructure, Low Earth Orbit (LEO)-based Non-terrestrial Networks (NTN) are poised to play a significant role in delivering global connectivity. To serve users effectively, multiple satellites must collaborate. Each satellite has only a brief window of time to communicate with the user. Therefore, an optimal handover strategy is needed to ensure seamless user transitions between satellites while minimizing unnecessary and frequent handovers, which leads to user satisfaction. However, existing handover strategies, primarily based on system geometry, may lack efficiency in dynamically changing user demand, particularly in deep urban canyon environments. This paper introduces a Spatio-temporal Entropy-based Scoring (STEBS) handover strategy. STEBS is a multi-objective dynamic handover strategy designed to minimize the number of handovers while consistently meeting user demand. Simulation results demonstrate that STEBS reduces the number of handovers and increases throughput, achieving an exceptionally low blocking rate of one-eighth compared to benchmark schemes, resulting in a $99 \%$ user satisfaction rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".