Propagation Models for GSM 900 and 1800 MHz for Port Harcourt and Enugu, Nigeria
Bibliographic record
Abstract
For the cellular networks to effectively cover a terrain or environment, accurate prediction of the coverage of the radio frequency signal is highly needed. Wave propagation models are essential and very important tools for determining the wave propagation characteristics for a particular environment. Path loss predictions are therefore required for the coverage planning, determination of multipath effects as well as interference and cell calculations. These calculations lead to high level network planning. Drive test measurements were taken along certain routes in Port Harcourt and Enugu, cities in Nigeria. These measurements were compared with calculated values from Okumura- Hata and COST231 Hata models. The average path loss values for the routes ranged from 135.01 db to 138.48 dB at 900 MHz, 142.26 db to 147.30 db at 1800 MHz. The standard deviations varied from 2.71 db to 15.94 db for the Okumura Hata model at 900 MHz whereas for the COST231 Hata model it was from 1.91 db to 15.04 db. Similarly, the mean square errors (µe) ranged from 0.8 db to 5.04 db for Okumura Hata at 900 MHz. For COST 231 Hata at this frequency, it was from 0.6 db to 4.76 db. This agrees with the acceptable International range. The acceptable range lies between 1? µ ? 15 db (Wu & Yuan 1998). The mean square error at 1800 MHz varied from 0.11 db to 5.40 db.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".