Quantum Adaptive Learning for Coverage Optimization in LEO Satellite Network
Bibliographic record
Abstract
A broader coverage of low earth orbit (LEO) satellite networks has been an object of research interest in recent years and will remain to be of keen interest to both start-ups and established companies in the future. However, achieving optimal coverage remains a challenge today, as the satellites dynamically move along their orbit, requiring frequent constellation size and beamsteering adjustments. Such frequent adjustments and the number of variables that need to be optimized result in a high computational complexity. Aiming to attend to these concerns, the present study proposes a quantum adaptive learning (QAL) as a potential solution for coverage optimization of stochastic geometry-based LEO satellite networks with low computational complexity by taking advantage of quantum computing and adaptive learning. The QAL scheme utilizes quantum computing and a feedback mechanism to improve the learning network design and parameters for higher accuracy with low computational complexity. As a study case, considering the binomial point process (BPP) distribution of contact distance between satellites and user terminals, we use the QAL scheme to optimize the satellites' constellation size and their corresponding beamsteering at each time step to achieve maximum coverage probability. To evaluate performance, the simulation results of the QAL scheme are compared with those of quantum machine learning (QML), which lacks feedback and adaptive mechanisms. The results show that the proposed QAL outperforms QML by achieving higher coverage probability, higher accuracy, and faster convergence.
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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 | 0.000 | 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".