Accurate Empirical Path Loss Models with Route Classification for mmWave Communications
Bibliographic record
Abstract
This paper presents accurate empirical path loss models with route classification for the high band frequency of 5 G wireless. Propagation path routes are mainly classified into line of sight (LOS) and non-line-of-sight (NLOS). The NLOS routes are classified into 2 separate routes, namely, Hard_NLOS and Soft_NLOS. Their path loss models include free-space loss (Lfs) and multiscreen diffraction loss (Lmsd) together with the reflection from the building blocks. However, these NLOS routes can be combined into a single formula. The path loss models were fitted with measured path loss data at frequencies of 28 GHz and 73 GHz. These models are compared with four 5G empirical models, namely 5GCM, 3GPP, METIS, and mmMAGIC. The results show that the separated route models provide good agreement, especially for the hard routes compared with those models and provide the minimum MAE of 4.45 dB, 4.34 dB, and 6.72 dB for the hard route, soft route, and an all-NLOS route, respectively, for the dual-band frequency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".