A Machine Learning Approach for the Prediction of Indoor Propagation Path-Loss in the Tera-Hertz Bands
Bibliographic record
Abstract
In this paper, we explore the use of machine learning (ML) models for predicting path loss in THz frequency bands for indoor environments. Traditional empirical and deterministic models often fall short in prediction accuracy. To overcome these limitations, we investigate four ML models: Gradient Boosting (GB), Random Forest (RF), Multivariate Polynomial (MP), and Deep Learning (ANN). Our simulation results indicate that the RF and ANN models outperform GB and MP models, reducing the Normalized Root Mean Square Error (NRMSE) by up to 25%, as discussed in detail in the results section. Furthermore, we introduce a hybrid learning approach, described as meta-learners, which combines elements of different ML models based on specific tasks. This hybrid model achieves an additional 10% improvement in NRMSE over the best-performing individual models. The algorithm used to calculate these values is provided, demonstrating the potential of meta-learners as effective predictors for enhancing path loss prediction in indoor THz communication scenarios.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".