Optimised COST-231 Hata Models for WiMAX Path Loss Prediction in Suburban and Open Urban Environments
Bibliographic record
Abstract
In Malaysia, the incumbent WiMAX operator utilises the bands of 2360-2390MHz to provide broadband services. Like all Radio Frequency (RF), WiMAX is susceptible to path loss. In this paper, field strength data collected in Cyberjaya, Malaysia is used to calculate the path loss suffered by the WiMAX signals. The measured path loss is compared with the theoretical path loss values estimated by the COST-231 Hata model, the Stanford University Interim (SUI) model and the Egli model. The best model to estimate the path loss based on the path loss exponents was determined to be the COST-231 Hata model. From this observation, an optimised model based on COST-231 Hata parameters is developed to predict path loss for suburban and open urban environments in the 2360-2390MHz band. The optimised model is validated using standard deviation error analysis, and the results indicate that the new optimised model predicts path loss in both suburban and open urban environments with very low standard deviation errors of less than 4.3dB and 1.9dB respectively. These values show that the model optimisation was done successfully and that the new optimised models will be able to determine the path loss suffered by the WiMAX signals more accurately. The optimised model may be used by telecommunication providers to improve their service.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".