On the Performance of Cooperative-Diversity Networks with the Nth Best-Relay Selection Scheme
Bibliographic record
Abstract
In this letter, we consider the adaptive decode-and-forward (DF) and amplify-and-forward (AF) cooperative-diversity systems with the Nthbest-relay selection scheme. In the best-relay selection scheme, from the set of M relays the best relay only forwards the source signal to the destination. However, the best relay might be unavailable; hence we might resort to the second, third or generally the Nth best relay. We derive closed-form expressions for the symbol error probability, outage probability and asymptotic error probability. In particular, we derive a closed-form expression for the probability density function (PDF) of the signal-to-noise ratio (SNR) of the relayed signal at the destination node. Then, we find a closed-form expression for the moment generating function (MGF) of the output SNR at the destination. This MGF is used to derive the closed-form expressions of the performance metrics. All these expressions are derived over identical and non-identical Rayleigh fading channels. Results show that with the Nthbest relay the diversity order is equal to (M - N + 2) where M is the number of relays.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".