Deterministic Modeling of Indoor Stairwells Propagation Channel
Bibliographic record
Abstract
A deterministic approach is presented for modeling a wireless propagation channel in a stairwell environment. The ray-tracing method is used to find the reflected and diffracted rays with the highest contribution to the total received power at a given location. Most significant reflected rays occur at the surfaces of specific steps of the stairwell and the surrounding lateral walls, while most significant diffracted beams emanate from the edges of steps located between transmitter and receiver antennas. The uniform theory of diffraction is used to calculate the diffracted contributions from the edges, whereas the multiple diffractions are calculated using heuristic diffraction coefficients. Unlike the reported models, the proposed model is deterministic and focuses on the radio propagation within stairwells at the X-band frequencies range. Experimental validation is performed using two types of antennas, horns and patches, for an operating frequency of 10 GHz. Simulated results of narrowband and wideband radio channels showed a good agreement with the experimental ones. The correlation coefficient between measured and simulation results is around 80% on average, and the residual errors between measured and simulated total received power results are less than 13%, which means that the proposed model has fair accuracy for such complex 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".