Performance of binary NCFSK with dual-branch S+N selection combining in rician and nakagami-m fading
Bibliographic record
Abstract
In traditional selection combining, the branch having the largest signal-to-noise ratio is employed for signal detection. However, many practical systems choose the branch with the largest signal-plus-noise (S+N) at the filter output. Recent work has studied the performance of noncoherent orthogonal frequency shift keying (NCFSK) with S+N selection combining in slow, flat Rayleigh fading channels. We consider the performance of NCFSK with dual-branch S+N selection combining in slow, flat Rician and Nakagami-m fading channels. Two S+N receiver structures for NCFSK are examined and analytical expressions for the bit error rate (BER) performances of these receivers are obtained both for Rician and Nakagami-m fading channels. The channel fadings are assumed to be independent, but not necessarily identically distributed. It is shown that both the S+N receiver structures considered outperform the traditional selection combiner and their performances are compared to the performances of equal gain and square law combiners.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".