Performance of L-branch diversity combiners in equally correlated Rician fading channels
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Bibliographic record
Abstract
Despite the importance of the Rician fading model in describing microcellular, picocellular and mobile satellite channels, few theoretical results are known about the performance of selection combining (SC) and equal gain combining (EGC) in correlated Rician fading channels. In this paper, we develop a novel approach for performance analysis of L-branch diversity systems in equally correlated Rician fading channels. This approach involves transforming a set of equally correlated branch gains into a set of conditionally independent branch gains, analyzing the performance for the independent case and averaging the conditional results. Consequently, we derive novel expressions for the average error rates of various digital modulations and the output moments for both SC and EGC. We find that the performance of diversity systems in correlated Rician fading channels can be worse than that in correlated Rayleigh fading channels, which has never been observed for the independent fading case.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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 it