Performance Analysis for NOMA-Assisted LEO Communications: A Two-Dimensional Stochastic Geometric Approach
Bibliographic record
Abstract
The integration of non-orthogonal multiple access (NOMA) into low earth orbit (LEO) systems has the potential to facilitate the ubiquitous coverage with high spectrum efficiency. To characterize the fundamental limits of NOMA assisted LEO systems, this paper proposes a model for downlink NOMA-LEO system via modelling the locations of terrestrial users and LEO satellites as two homogeneous spherical Poisson point processes. In particular, a typical satellite uses NOMA to simultaneously serve the nearest and the farthest users within its visible range. The novelty of this paper is to first introduce an equivalent two-dimensional model that can significantly simplify the performance analysis of LEO systems. Then, considering that the satellite-terrestrial channel follows the Nakagami-mfading, the closed-form expressions of the user association and the visible probability are studied under the scenario where the number of users visible to a randomly selected satellite is greater than one. Subsequently, the derived results are utilized to analyze the approximate moments of the conditional success probability and the signal-to-interference-plus-noise ratio Meta distribution for both NOMA-LEO and orthogonal multiple access (OMA) LEO transmissions. Finally, the numerical results demonstrate that:1)Asymmetric target rates can achieve a performance gain of NOMA over OMA in terms of the link reliability and the coverage probability, while symmetric settings still have merit for NOMA if there is a low requirement for reliability; and2)Enhancements in the link reliability and the coverage probability are achievable through improvements in channel quality and reductions in orbital altitude and density. However, improving path loss develops the coverage probability but may not always yield an increase in the link reliability.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".