Dual-hop AF systems with maximum end-to-end SNR relay selection over nakagami-m and rician fading links
Bibliographic record
Abstract
Novel, exact closed-form expressions are derived for the probability density function (PDF) and the cumulative distribution function (CDF) of the instantaneous end-to-end signal-to-noise ratio (SNR) of opportunistic dual-hop amplify-and-forward relaying systems with maximum end-to-end SNR relay selection. The derived expressions are used to find exact integral solutions for the ergodic capacity and the average symbol error probability as well as an exact explicit closed-form solution for the outage probability of the opportunistic AF system. The analysis is presented for the common channel fading distributions, Nakagami-m and Rician fadings, and for the cases of statistically identical fading links and statistically non-identical fading links. Examples show precise agreement between analytical results and simulation results. It is shown that the system performance is superior to AF relaying systems without relay selection. The maximum end-to-end SNR relay selection method provides diversity gain, proportional to the relay selection pool size, over AF relaying systems without relay selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".