Performance analysis of poisson cellular networks with lognormal shadowed Rayleigh fading
Bibliographic record
Abstract
This paper analyzes downlink coverage probability and spectral efficiency of Poisson cellular networks with lognormal shadowed Rayleigh fading, provided each user is associated to the closest base station (BS). Both location-dependent and cell area wide aspects of the coverage probability and spectral efficiency metrics are presented. Performance impact of system parameters such as frequency reuse factor, transmission probability, and Signal to Interference Ratio (SIR) gap from Shannon capacity are characterized. Numerical results support the view that shadowing significantly degrades the performance. The cell area wide spectral efficiency decreases by 37% when the shadowing standard deviation increases from 0dB to 12dB. Finally, the derived results on location-dependent metrics are applied to Fractional Frequency Reuse (FFR) optimal partitioning in terms of system spectral efficiency, which is found dependent on system parameters such as SIR gap from Shannon capacity. It is also numerically shown that the FFR significantly improves the (link) spectral efficiency for cell edge users. For a user far away from its associated BS (at a distance 3 times the radius of average cell area) and considering SIR gap from Shannon capacity of 3dB, FFR(1,3) improves the (link) spectral efficiency by 239% compared to the universal reuse factor.
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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 | 0.008 |
| 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".