Comparative Study of VPE-Driven CNN Models for Radio Wave Propagation Modeling in Tunnels
Bibliographic record
Abstract
Radio wave propagation modeling in railway environments is of fundamental importance in designing reliable train communication systems. In recent years, many machine learning (ML) techniques have been applied to accelerate the modeling process. In particular, convolutional neural networks (CNNs) have presented a superior performance in extracting features and reconstructing field distribution. However, the relevant literature is still missing a comprehensive study on CNN architecture design and the performance of different CNN models. In this article, we compare the performance of nine different CNNs, including recently developed advanced CNN techniques, for radio wave propagation modeling in tunnels. Each model is assessed in three different size variants to examine the effect of model complexity on performance. The CNN model is driven by a vector parabolic equation (VPE) channel simulator based on super-resolution. In addition, we investigate the performance of hybridizing various CNN architectures and present a CNN design roadmap for radio wave propagation modeling in tunnels. Besides, the proposed models are validated against measurement campaigns in two realistic tunnels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".