Stochastic Geometry Modeling of Cellular Uplink Power Control under Composite Rayleigh-Lognormal Fading
Bibliographic record
Abstract
Power control for uplink transmission in a randomly laid-out cellular network operating over an environment with path loss and composite Rayleigh-lognormal shadowing is investigated. Each mobile station (MS) adjusts its transmit power to completely remove shadowing and to partially invert the effect of path loss. Using stochastic geometry tools, we derive an approximate coverage probability expression and validate it via simulations. With the power control scheme considered, shadowing with lower standard deviation improves the coverage probability. Also the severity of shadowing of local and surrounding environments has the same effect on the coverage probability. It was also observed that at low signal-to-interference-plus-noise ratio (SINR) thresholds, complete compensation of shadowing and partial compensation of path loss improves coverage, while at high SINR thresholds inverting only the effect of shadowing gives a better coverage probability.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".