Simplified Performance Analysis for Amplify-and-Forward Cooperative Diversity Optimal Detection of Binary Signals with Symmetric Alpha-Stable Noise
Bibliographic record
Abstract
For a wide range of wireless channels, symmetric alpha-stable (SαS) is shown to be applicable to model the noise and/or the interference. However, the ongoing research of the optimal detection of the cooperative diversity in alpha-stable noise is limited to few closed-form expressions for special values of the characteristic exponent due to the unavailability of the probability density function of the noise. This limits the performance analysis of such networks. In this paper, to overcome this difficulty, we use the compound property of the SαS distribution to derive an approximate analytical expression for the error probability. This facilitates the calculation of the error performance of the cooperative amplify-and-forward diversity with optimal detection of binary signals in SαS noise for any value of . Computer simulations are used to verify the correctness and accuracy of the derived analytical error results. Our mathematical modeling and simulations prove that asymptotic maximum diversity order is achievable.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".