Bibliographic record
Abstract
Closed-form expression for the outage probability of dual-hop amplify-and-forward (AF) systems with relay selection from N independent and non-identically (i.ni.d.) distributed dual-hop branches is obtained. Closed-form expression for the probability density function of the end-to-end signal-to-noise ratio (SNR) is also obtained for such systems, and is used to obtain the exact average symbol error probability for systems operating over i.ni.d. Nakagami-m fading links. It is found that the limiting slopes of the outage probability and the average symbol error probability curves are proportional to the sum of the minimums of the fading parameters of the source-to-relay and relay-to-destination links of each branch. The modified generalized transformed characteristic function (M-GTCF) is used to obtain exact, integral solutions for the outage probability and the average symbol error probability of multihop AF relaying systems operating over N i.ni.d. Nakagami-m fading links. It is found that the limiting slopes of the performance metrics curves are proportional to the minimum of the fading parameters of the individual links. Dual-hop AF systems with relay selection from N available relays are compared to multihop AF systems with N intermediate relays. It is found that dual-hop AF systems can significantly outperform multihop AF systems. The findings are used to propose multiple criteria for practical design of wireless cooperative systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".