Revised Analyses of Postdetection Switched Combining in Nakagami-<tex>$m$</tex>Fading
Bibliographic record
Abstract
In two recent papers, the performances of dual-branch postdetection switch-and-stay combining (SSC) for noncoherent orthogonal binary frequency-shift keying (BFSK) and noncoherent orthogonal M-ary frequency-shift keying (MFSK) operating in the presence of slow flat fading modeled by Rayleigh, Nakagami-m, and Rician distributions have been analyzed. In this paper, we show that these previous analyses for the Nakagami-m fading model, which are restricted to integer values of m, are incorrect, and we derive the correct bit-error rate (BER) performances of BFSK and MFSK with dual-branch SSC in Nakagami-m fading for all values of m. Optimum switching thresholds that minimize the BER of BFSK and MFSK with postdetection SSC in Nakagami-m fading are obtained. The performance of postdetection SSC is compared with the performance of predetection SSC, and it is shown that postdetection SSC outperforms predetection SSC for all values of signal-to-noise ratio (SNR). We also show that for a given BER, the performance gain of postdetection SSC over predetection SSC has been overestimated by several decibels in SNR in previous publications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".