Performance Analysis of Path loss Prediction Models in Wireless Mobile Networks in Different Propagation Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it