On the Benefits of Decorrelation in Dual-Branch Diversity
Bibliographic record
Abstract
The performance of a dual-branch decorrelator receiver operating in correlated Rayleigh and Rician fading channels in conjunction with selection combining (SC), square- law combining (SLC) and equal gain combining (EGC) diversity is analyzed and compared to the performance of a conventional SC, SLC and EGC diversity receiver. Analytical expressions for the average symbol error rate (SER) and the average bit error rate (BER) of several modulation techniques of practical interest, the mean output signal-to-noise ratio (SNR) and the outage probability are derived. It is shown that the decorrelator receiver has superior performance over the conventional receiver by as much as 2.1 dB in average SNR when SC is employed. Interestingly, it is also shown that the mean output SNR of the decorrelator SC receiver improves with increasing the correlation coefficient while that of the conventional SC receiver degrades with increasing the correlation coefficient. In Rician fading, the results indicate that the outage probability of the decorrelator receiver is much less than the outage probability of the conventional receiver and the gap between the two increases as the channels become less faded.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".