Error Rate of Quadrature Subbranch Hybrid Selection/ Maximal-Ratio Combining in Rayleigh Fading
Bibliographic record
Abstract
The symbol-error rate (SER) of a quadrature subbranch hybrid selection/maximal-ratio combining (QBHS/MRC) scheme for 1-D modulations in Rayleigh fading is examined. At the receiver, N diversity branches are split into 2N in-phase and quadrature subbranches. Traditional hybrid selection/maximal-ratio combining (H-S/MRC) is then applied over the 2N subbranches. M-ary pulse amplitude modulation, including coherent binary phase-shift keying, with QBHS/MRC is studied. The SER performances of QBHS/MRC and H-S/MRC are compared. Results show that QBHS/MRC systems outperform traditional H-S/MRC 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".