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 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 | 0.001 | 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".