Machine Learning Based Delay Spread Prediction in Underground Mine
Bibliographic record
Abstract
This paper presents a machine learning-based approach for predicting the Root Mean Square Delay Spread (RMSDS) in underground mine environments. Real-world measurements at 28 GHz were collected in the Lamaque gold mine, utilizing a virtual massive MIMO array and a frequencydomain channel sounder to characterize the multipath behavior. The propagation complexity in the confined mine gallery, with its waveguide effects and high-order reflections, presents specific challenges for traditional modeling. An XGBoost-based regression model was developed, using features such as separation distance,$\mathbf{R x}$array coordinates, and operating frequency. The model achieved an RMSE of 0.87, MAE of 0.69,$\mathbf{R}^{\mathbf{2}}$of 0.52, and a correlation coefficient of 0.75, indicating moderate prediction accuracy. While the results demonstrate the potential of machine learning for delay spread estimation, they also highlight the limitations in capturing complex propagation effects. The study underscores the need for incorporating additional environmental parameters to enhance model robustness and reliability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".