Bit-error-probability for non-coherent orthogonal signals in fading with optimum combining for correlated branch diversity
Bibliographic record
Abstract
The paper presents an analysis of the bit-error probability for optimal receivers in which the diversity branches are correlated. Non-coherent orthogonal digital modulation (NCODM) with Rician and Rayleigh slow, non-selective fading models are assumed. The maximum likelihood diversity combining laws are derived and simple implementation structure is deduced. The authors find that Rayleigh fading can be better than Rician fading in correlated diversity environments: a situation quite different from the independent diversity case. Also, for the Rayleigh fading model with correlated branch diversity, an equal-weight, square-law combiner usually has the same error performance as the more complex optimum combiner. However, the authors find that this is not the case for a Rician fading model with the same correlation environment. Compensation schemes for the lossy effect of the correlation are designed and found effective when the dominant noise and interference have almost the same correlation distribution as the fading signals. The diagonalization of quadratic forms is used both for error probability analysis and for optimal diversity receiver simplification.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".