Handover Strategy for LEO Satellite Networks Using Bipartite Graph and Hysteresis Margin
Bibliographic record
Abstract
The low Earth orbit (LEO) satellite constellation has become a highly effective solution for non-terrestrial networks (NTN), offering reliable, uninterrupted, and high-speed global communication. However, the rapid movement of LEO satellites results in a significant handover rate across satellites. Therefore, satellite handover management is essential to ensure the stability and continuity of communication services. This paper proposes a novel weighted bipartite graph-based handover strategy in LEO constellations to enhance quality of service (QoS) and overcome the challenge of frequent satellite handovers. The proposed approach utilizes the Kuhn-Munkres (KM) algorithm to achieve optimal matching with maximum weight, thereby ensuring efficient load distribution and high-quality communication. Moreover, the implementation of hysteresis margin (HM) reduces unnecessary handovers and thus enhances the overall performance of the network. The numerical results demonstrate a significant reduction in handover rate and latency, while improving energy efficiency and achieving enhanced data rates. In particular, our scheme effectively adapts to varying Rician K-factors and demonstrates flexibility under different signal conditions. Furthermore, the obtained results highlight a significant reduction in handover costs and ensure efficient and reliable communication in LEO satellite networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".