Fade Slope Analysis of Ka-Band Earth-LEO Satellite Links Using a Synthetic Rain Field Model
Bibliographic record
Abstract
Because the motion of a low Earth orbit (LEO) satellite across the sky causes the Earth-space path to very quickly pass through any rain cells in the vicinity, the degree of rain fading on such paths changes more rapidly and leads to steeper fade slopes than in the geostationary case. Because comprehensive measurement data have not yet been compiled for fading on LEO links in the Ka-band, we have used simulations based on Goldhirsh's method for determining the key parameters of the well-known EXCELL model of a horizontal rain structure from long-term global rain statistics to obtain plausible estimates of the fade slope distributions for selected scenarios. The results that we obtained for geostationary satellites closely match those observed at selected sites during the Advanced Communications Technology Satellite program. The results that we obtained for LEO satellites show how fade slopes will steepen as 1) the altitude of the satellite decreases; 2) the frequency band of operation increases; and 3) the average rain rate increases. Furthermore, they suggest that, at a given probability level, the fade slopes could be between two and ten times greater than those for geostationary satellites and that mobile terminals with a clear view of the sky will experience fade slopes that are similar to those encountered by fixed or transportable terminals. These results have important implications for the design of power control algorithms and other fade-mitigation techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".