Coverage Analysis of SAGIN With Sectorized Beam Pattern Under Shadowed-Rician Fading Channels
Bibliographic record
Abstract
Space-air-ground integrated networks (SAGIN) have become a research hotspot facing the next generation of communications. The theoretical analysis for non-terrestrial networks (NTN) is significant before applying them in practical scenarios, but the existing works failed to provide a general analysis approach for NTN. Against this background, multiple satellites and civil aircrafts (CAs) are modeled as 3-D binomial point processes (BPPs) in the given finite space in this paper, and we desire to investigate the coverage performance of downlink CA augmented-SAGIN (CAA-SAGIN). Considering the sectorized beam pattern of platforms, we provide a detailed analysis of the different distributions of the serving and interfering platforms and derive the Laplace transform of the interference under shadowed-Rician fading channels. Then, the exact and closed-form expressions are obtained for the general cases with interference and the particular cases without interference via stochastic geometry. The approximations and boundary values are derived by adopting the existing mathematical theories. We analyze the effects of different parameters on the coverage probability of satellite and CA networks, and prove the validity of the derived analytical expressions, approximations, and bounds. Moreover, this work paves the way from the system level to exploit the generic coverage performance of NTN.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".