Landmobile Radiowave Multipaths' DOA-Distribution: Assessing Geometric Models by the Open Literature's Empirical Datasets
Bibliographic record
Abstract
¿Geometric modeling¿ idealizes the spatial geometric relationships among the transmitter, the scatterers, and the receiver in a wireless propagation channel - to produce closed-form formulas of various channel-fading metrics (e.g., the distribution of the azimuth angle-of-arrival of the arriving multipaths). Scattered in the open literature are numerous such ¿geometric models,¿ each advancing its own closed-form formula of a fading metric, each based on a different idealization of the spatial geometry of the scatterers. Lacking in the open literature is a comprehensive and critical comparison among all such single-cluster geometric-model-based formulas of the arriving multipaths' azimuth direction-of-arrival distribution. This paper fills this literature gap. The comparison here uses all empirical data legibly available in the open literature for landmobile wireless radiowave propagation. No one geometric model is best by all criteria and for all environments. However, a safe choice is the model with a Gaussian density of scatterers centered at the transmitter. Despite this model's simplicity of having only one degree of freedom, it isalwayseither the best fitting model or offers an LSE within one third of an order-of-magnitude as the best fitting model for all empirical dataset of all environments.
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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 | 0.025 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".