A general approach for evaluating the outage probability in microcellular mobile radio systems
Bibliographic record
Abstract
A unified expression for computing the probability of outage in cellular mobile radio systems is derived. The method handles non-integer Nakagami fading indexes, unequal Rice factors, dissimilar shadowing spreads, unequal transmit powers as well as all the common fading distributions (Rayleigh, Rice, Nakagami-m, Nakagami-q, lognormal-Rice, Suzuki and lognormal-Nakagami-m). The exact outage probability is expressed in terms of a finite-range integral. The integral can also be approximated by a Gauss-Chebychev quadrature (GCQ) formula requiring the knowledge of the moment generating function (MGF) at only a small number of points. An estimate of the remainder term is also derived. This technique lends itself to a powerful tool for outage analysis since it does not impose any restrictions while being easy to program. Some previous studies have suggested approximating Rician desired signal statistics by a Nakagami-m model to circumvent the difficulty in evaluating the outage in Rician fading. We asses the suitability of this approximation by providing a comparison study of the outage performance in these two fading conditions. Surprisingly, some basic results for Nakagami-m channel have been overlooked, which has led to misleadingly optimistic results with the Nakagami-m approximation model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".