On the Performance of Amplify-and-Forward Cooperative Diversity with the Nth Best-Relay Selection Scheme
Bibliographic record
Abstract
Cooperative-diversity networks have been proposed as a way to form virtual antenna arrays without using collocated multiple antennas. In this paper, we consider the amplify-and-forward cooperative-diversity system with theNthbest-relay selection scheme. In the best-relay selection scheme, 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 theNthbest relay. We derive closed-form expressions for the symbol error probability, outage probability and channel capacity. In particular, we derive a closed-form expression for the probability density function of the signal-to-noise ratio of the relayed signal at the destination node. Then, we find a closed-form expression for the moment generating function of the total SNR at the destination. This MGF is used to derive the closed-form expressions of the performance metrics. Results show that with theNthbest relay the diversity order is equal to (M-N+ 2) whereMis the number of relays. Simulation results are also given to verify the analytical results.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".