Evaluation of outage probability for uniformly distributed users based on signal‐to‐interference‐plus‐noise ratio
Bibliographic record
Abstract
Improving the performance of wireless networks to maintain good quality of service for all users is always an ultimate goal. To reach this goal, the performance should be evaluated accurately and then analysed. Geometric modelling of wireless networks is drawing significant attention with regard to analytically evaluating the performance. In this study, stochastic modelling of users is used to emulate their distribution in wireless networks, which is analysed when only one direct link is connecting the base station to each user over the cell. Hence, the final findings will be more universal and applicable for different types of mobile cellular networks. Probability density function (PDF) of signal‐to‐interference ratio, and signal‐to‐noise ratio are evaluated, and used to find the PDF of signal‐to‐interference‐plus‐noise ratio (SINR) in a uniform filed of interference. Then, the outage probability based on SINR is evaluated, since it is one of the important factors in studying the performance of wireless networks. Finally, simulations are performed in order to validate the analytical results.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".