Data-Driven Prediction of Tunnel Propagation Characteristics Using Synthetic Simulations and Machine Learning
Bibliographic record
Abstract
Accurate modeling of tunnel propagation at millimeter-wave (mmWave) frequencies is essential for designing robust 5G/6G networks in underground environments. Conventional models such as FSPL and ITU-R P.1238 lack the flexibility to generalize across varying tunnel geometries, materials, and frequencies. This paper presents a simulation-driven machine learning framework that predicts path loss, delay spread, and spectral capacity using synthetically generated tunnel scenarios. A Random Forest regressor is trained on over 1,000 parameterized samples spanning 28, 38, and 60 GHz. The model achieves a mean absolute error (MAE) of 0.50 dB for path loss, outperforming FSPL by over 90%. Feature importance analysis highlights the dominant influence of curve angle and obstacle density. Frequency-based performance trends are also quantified. The proposed method offers a scalable, interpretable alternative to measurement-based modeling for tunnel-specific wireless planning.
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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".