LoS-Aware Handover Uplink NOMA Transmissions for Multi-Layer LEO Satellite Constellation
Bibliographic record
Abstract
Mega low earth orbit (LEO) high throughput satellite (HTS) constellations are regarded as one of the most important development shifts in the next generation of mobile communication systems in both industry and academia. Consider the short duration of line-of-sight (LoS) link and high dynamic topology of LEO HTSs, we propose a handover uplink non-orthogonal multiple access (Hu-NOMA) transmission scheme for a multi-layer LEO HTS constellation. First, we formulate a practical two-layer LEO HTS constellation, where the higher-layer LEO HTS has a longer LoS link duration but not always visible, and the lower-layer LEO HTS has a shorter LoS link duration and can continuous support the uplink transmission via frequent handovers. Then, we derive the closed-form expressions of ergodic capacity (EC) and outage probability (OP) for both NOMA and orthogonal multiple access (OMA) schemes. Further, we propose an improved ergodic capacity (IEC) NOMA algorithm, and terrestrial user equipments (UEs) can perform our IEC Hu-NOMA transmission according to their exponential distributed random service time, which can achieve higher EC, and have similar OP compared to the conventional OMA scheme but reduce half of the transmission time slot. Simulation results validate the accuracy of our theoretical derivations, and show that our IEC Hu-NOMA can outperform the state-of-art schemes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".