Postdetection switch-and-stay combining in Nakagami-m fading
Bibliographic record
Abstract
In two recent papers (Alouini and Simon (2003), Simon and Alouini (2003)), the performance of dual branch post-detection switch-and-stay combining (SSC) for noncoherent binary orthogonal frequency-shift keying (BFSK) and noncoherent M-ary orthogonal frequency-shift keying (MFSK) operating in the present of slow flat fading modeled by Rayleigh, Nakagami-m, and Rician distributions have been analyzed. This paper shows that the previous analyses of BFSK and MFSK with postdetection SSC in Nakagami-m fading, which was limited to integer values of m, are incorrect and we derive correct bit error rate performance results for BFSK and MFSK with dual-branch SSC in Nakagami-m fading for all values of m. Our analytical results are verified using extensive Monte Carlo simulations. It is shown that for a given bit error rate the performance gain of postdetection SSC over predetection SSC has been overestimated by several dB in SNR in previous reported work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".