Integrating Non-Orthogonal Multiple Access into Low Earth Orbit Satellite Systems
Bibliographic record
Abstract
This paper proposes a downlink non-orthogonal multiple access (NOMA) low earth orbit (LEO) satellite system by modeling the locations of terrestrial users and LEO satellites as two homogeneous spherical Poisson point processes, respectively. Firstly, considering that the satellite-terrestrial channel follows the Nakagami-m fading, the closed-form expressions of the user association and the visible probability are studied under the scenario where the number of user visible to the random selected satellite is greater than one. Subsequently, the above results are adopted to analyze the approximate results for the moments of the conditional success probability and the signal-to-interference-plus-noise ratio Meta distribution. The numerical results demonstrate that the asymmetric target rates can realize a performance gain of NOMA over orthogonal multiple access in terms of the link reliability and the coverage probability, while symmetric settings still have a merit for NOMA when there is a low requirement for the link reliability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".