A Novel Joint Multistage Handover Strategy for DAPS-enabled LEO Satellite Networks
Bibliographic record
Abstract
Owing to the exponential growth in the number of satellites, developing effective handover strategies and resource allocation techniques has become crucial for Low Earth Orbit (LEO) satellite networks. In response to the high data-rate demands of User Equipment (UE), we propose an LEO-based Dual Activate Protocol Stack handover scheme (LDAPS). This innovative architecture leverages the DAPS mechanism to enhance throughput, reduce handover interruptions, and ensure seamless connectivity for UEs. The initial problem is decomposed into two subproblems: employing an Alternating Direction Method of Multipliers (ADMM) to address bandwidth allocation and utilizing a simulated annealing algorithm to optimize the handover strategy. Furthermore, we introduce a multi-stage handover system that accommodates dynamic propagation conditions between satellites and UEs. We validate the performance of the proposed methods for bandwidth allocation and handover optimization through extensive comparative studies. Our results demonstrate that these algorithms significantly outperform baseline methods, highlighting their potential to enhance user experience and optimize system performance 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".