Fade Slope Analysis of Ka-Band LEO Satellite Links
Bibliographic record
Abstract
Because the motion of a LEO satellite across the sky causes the earth-space path to pass through any rain cells in the vicinity very quickly, the degree of rain fading on such paths changes more rapidly and leads to steeper fade slopes than in the geostationary case. This has important implications for the performance of the power control algorithms and other techniques used to mitigate such fading. Because comprehensive measurement data have not yet been compiled for fading on LEO links at Ka-band, we must resort to simulation to predict the statistics of such fading. Here we use an approach based upon the well-known EXCELL model of horizontal rain structure and Goldhirsh's method for obtaining the parameters of the EXCELL model from ITU-R rain zone parameters. As the satellite sweeps across the sky, we determine the intersection of the earth-space path with rain cells in the vicinity. Given the corresponding rain rates, and accounting for attenuation by atmospheric gases, cloud and fog, we predict the total attenuation along the path at each instant during the pass. Our results suggest that: (1) fade slopes observed on earth-space links to LEO can be between two and ten times greater than those reported previously for the geostationary case, (2) fade slopes will become steeper as the altitude of the satellite decreases, (3) fade slopes will become steeper with increasing carrier frequency, (4) rising and falling fade slopes of a given value are equally likely and (5) the probability of exceeding a given fade slope value tends to increase rapidly in regions with higher average rain rates.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".