A unified approach to computing error probabilities of diversity combining schemes over correlated fading channels
Bibliographic record
Abstract
The average bit-error rate performance of one-stage and two-stage diversity combining schemes operating over correlated fading channels is investigated. Two channel models that can significantly simplify the performance analysis are considered. In particular, a linear correlation channel model having equal branch variances can be decorrelated at the receiver, so that the branches become independent. It is shown that, in general, employing diversity combining schemes for decorrelated or orthogonalized branches can recover some of the diversity gain lost due to the branch correlations. This is observed, for example, for the case of hybrid selection/maximum ratio combining operating over decorrelated and orthogonalized non-zero mean Gaussian fading channels. Furthermore, a fading amplitude channel model is proposed assuming vector norm superposition of the impinging plane waves. This channel model is well-suited for the performance analysis of maximum ratio and equal gain combining schemes operating over correlated fading channels. Finally, the average bit error rates of several diversity combining schemes are evaluated analytically using the Prony approximation method as well as using computer simulation.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".