Unified Stochastic Geometry Modeling and Analysis of Cellular Networks in LOS/NLOS and Shadowed Fading
Bibliographic record
Abstract
Statistical characterization of the signal-tointerference-plus-noise ratio (SINR) via its cumulative distribution function is ubiquitous in a vast majority of technical contributions in the area of cellular networks, since it boils down to averaging the Laplace transform of the aggregate interference, a benefit accorded at the expense of confinement to the simplistic Rayleigh fading. In this paper, to capture diverse fading channels that arise in realistic outdoor/indoor wireless communication scenarios, we tackle the problem differently. By exploiting the moment generating function of the SINR, we succeed in analytically assessing cellular networks performance over the shadowed κ-μ, κ-μ, and η-μ fading models. These channel models offer high flexibility by capturing diverse fading channels, including Rayleigh, Nakagami-m, Rician, and Rician shadow fading distributions. These channel models have been recently promoted for their capability to accurately model dense urban environments, future femtocells, and device-to-device shadowed channels. In addition to unifying the analysis for different channel models, this paper integrates the coverage, the achievable rate, and the bit error probability, which are largely treated separately in the literature. The developed model and analysis are validated over a broad range of simulation setups and parameters.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".