Canonical error rate analysis for maximal ratio diversity in correlated fading
Bibliographic record
Abstract
Simple closed-form bit error rate expressions for both coherent and noncoherent modulation schemes using maximal ratio (MR) combining with arbitrarily correlated Nakagami (1960) fading channels or arbitrarily correlated Ricean fading channels have been derived. The expressions for M-ary noncoherent frequency shift keying (MNCFSK) and for differential phase shift keying (DPSK) are exact. The expressions for coherent binary phase shift keying (BPSK) and coherent binary frequency shift keying (CBFSK) are very nearly exact for a large useful range of signal-to-noise ratio (SNR) values (i.e. SNR=0 to 60 dB). The usefulness of these results lies in their ease of application; they can be used with any correlation matrix and the functions are very easy to evaluate. The general method introduced here may be applied to very easily derive closed-form expressions for other modulation schemes.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".