Simulation of Rainfall Attenuation Prediction Models for the Optimization of High-Speed Links in Millimeter Bands (80GHz): Tropical Regions’ Case
Bibliographic record
Abstract
We deal with the problem of attenuation of electromagnetic waves due to precipitation. Indeed, the millimeter wave frequency (30–300 GHz) is a promising candidate for broadband wireless networks (4G and beyond), but atmospheric elements limit radio links to this frequency band. Precipitation is the important atmospheric element that causes the attenuation of a wave during its propagation and should therefore be estimated for the proper functioning of a link. It is with this in mind that our study focuses on the choice of an appropriate model for predicting the attenuation of a radio link after simulation of several models proposed in the literature in order to assess the impact of precipitation in the tropical region. The method used to achieve this objective consists firstly of analysing existing models and determining theoretical and actual unavailability using ITU-R methods and the Nomad monitoring tool; then secondly, we develop an application to evaluate the attenuation due to rain using the Da Silva, Mello and ITU-R models. The calculated attenuations were compared to experimental measurements, and Da Silva's model was found to be the best fit. On a local level, these results have a significant impact on the planning and optimization of communications infrastructures, enabling better performance of networks using millimeter waves. At the national level, this contributes to improved telecommunications networks and more reliable connectivity. Globally, this research can aid the development of high-frequency wireless communications in tropical regions, thereby promoting large-scale technological and economic advancement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".