On the Statistics of the Sum of Correlated Generalized-K RVs
Bibliographic record
Abstract
Appropriate channel modeling plays an important role in the design and analysis of various transmission and reception schemes over composite fading channels. The generalized-K (Gamma-Gamma) composite fading model has been used recently to model composite fading in wireless channels as an alternative to the less tractable lognormal-based models. In this paper, the expression of the amount of fading for the sum of correlated generalized-K random variables is derived and then the moment matching method is used to approximate, in the lower tail region, the probability density function of the sum of identically distributed generalized-K random variables with positively and equally correlated shadowing components by the familiar Gamma distribution. Furthermore, the obtained expressions of the amount of fading give insights into the effect of shadowing correlations on the performance of maximal ratio combining receivers in coordinated multi-point transmission and reception schemes in future wireless systems.
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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".