Performance bounds for diversity receptions over a new fading model with arbitrary branch correlation
Bibliographic record
Abstract
Abstract The performance of a new (Beaulieu-Xie) fading model is analyzed using bounds. This recently proposed fading model can be used to describe both line-of-sight and non-line-of-sight components of a fading channel having different diversity orders. We consider the outage probability and error rate performance of maximal ratio combining, equal-gain combining, and selection combining over arbitrarily correlated Beaulieu-Xie fading channels. Closed-form expressions for upper and lower bounds to the outage probability and error rate are obtained, and it is shown that these bounds are asymptotically tight in the high signal-to-noise ratio regime. The analytical results are verified via Monte Carlo simulations. It is shown that the Beaulieu-Xie fading model can be more useful than the Ricean and Nakagami-m fading models in characterizing environments with both line-of-sight and multiple reflected specular components.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".