A Gaussian Beam Approximation Approach for Embedding Antennas Into Vector Parabolic Equation-Based Wireless Channel Propagation Models
Bibliographic record
Abstract
Vector parabolic equation (VPE) methods have been widely applied to the modeling of radio-wave propagation in tunnel environments, offering high computational efficiency and fidelity. While the propagation environment can be discretized and represented in detail, the representation of radiating sources (such as transmitting antennas) requires the calculation, analytical if possible or numerical via another method such as ray-tracing (RT), of the fields that the sources generate on the initial plane of the VPE model. These initial conditions are necessary for subsequently applying VPE. However, the solutions offered so far compromise either the accuracy or the efficiency of VPE. For example, generating the initial conditions for VPE through RT adds significant computational overhead to the typically fast VPE solver. To address this significant limitation of VPE methods, we introduce a technique that allows one to directly embed antennas into a VPE mesh, via a Gaussian beam approximation of their radiated fields. Hence, the initial conditions for VPE are generated for practical antenna patterns, without invoking other techniques and with no compromise on the inherent efficiency of VPE. Concrete guidelines on how to choose parameters for Gaussian beams are provided. Numerical results are compared to experimental measurements in various tunnel scenarios, demonstrating the validity and usefulness of the technique.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".