Realistic Channel and Delay Coefficient Generation for Dual Mobile Space-Ground Links: A Tutorial
Bibliographic record
Abstract
Channel and delay coefficient are two essential parameters for the characterization of a multipath propagation environment. It is crucial to generate realistic channel and delay coefficient in order to study the channel characteristics that involves signals propagating through environments with severe multipath effects. While many deterministic channel models, such as ray-tracing (RT), face challenges like high computational complexity, data requirements for geometrical information, and inapplicability for space-ground links, and nongeometry-based stochastic channel models (NGSCMs) might lack spatial consistency and offer lower accuracy, we present a scalable tutorial for the channel modeling of dual mobile space-ground links in urban areas, utilizing the Quasi Deterministic Radio Channel Generator (QuaDRiGa), which adopts a geometry-based stochastic channel model (GSCM), in conjunction with an International Telecommunication Union (ITU) provided state duration model. This tutorial allows for the generation of realistic channel and delay coefficients in a multipath environment for dual mobile space-ground links. We validate the accuracy of the work by analyzing the generated channel and delay coefficient from several aspects, such as received signal power and amplitude, multipath delay distribution, delay spread and Doppler spectrum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".