Small-scale fading modeling for tactical ad hoc networks
Bibliographic record
Abstract
Small-scale fading describes the rapid fluctuations of the channel envelop due to multipath (interferences between different components of the transmitted signal) and Doppler spectrum. The modeling of these effects is not easy especially in context of mobile wireless ad hoc networks (MWAN). The nodes' mobility in an MWAN induces random and rapid variation of network topology. Consequently, small-scale fading modeling becomes more complex. We elaborated a channel simulator using Tap Delay Lines (TDL) method and based on ITU-RM.1225 model. ITU standard model is used in addition with Doppler spectrum formulation particularly for mobile-to-mobile (M2M) channel. Some simulation results with the corresponding channel statistics (CDF, LCR and ADF) are presented. This simulator is a part of a global approach which linked tactical scenarios specification and channel modeling (large-scale and small-scale propagation). The aim is to provide better analysis of tactical ad hoc network channel modeling.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".