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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".