Performance Analysis of Path loss Prediction Models in Wireless Mobile Networks in Different Propagation Environments
Bibliographic record
Abstract
for cell design, path loss is a very important issue and has been studied for a long time. For the new generation of mobile networks, innovative prediction models with extended frequencies are needed. The goal of this paper is to analyze six different path loss prediction models: free space, extended COST-231 Hata, empirical Hata, Walfisch-Ikegami, Stanford University Interim (SUI) and Ericson (9999). The article shows that SUI, Ericsson and Empirical Hata are overall the best choice for the new generation of mobile networks regardless of distance and type of environment. However, SUI outperforms Ericsson and Empirical Hata for 3.5 GHz for both urban and suburban environments. For higher frequencies (28 GHz), which is needed for the new generation of mobile networks like 5G, it is shown that Ericsson model gives better results for path loss in urban compared with SUI which gives better results in suburban environment. This conclusion is confirmed by introducing the average of path loss.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| 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".