Exact Analysis of Dual-Hop AF Maximum End-to-End SNR Relay Selection
Bibliographic record
Abstract
New, exact closed-form expressions are derived for the probability density function and the cumulative distribution function of the end-to-end signal-to-noise ratio (SNR) of opportunistic dual-hop amplify-and-forward (AF) relaying systems with relay selection. The expressions are used to obtain the first exact integral solutions for the ergodic capacity and average symbol error probability, and the first exact closed-form solution for outage probability of an opportunistic AF relaying system where the best node is selected from a number of candidate intermediate nodes to relay the data signal between the source and the destination. The selection follows a maximum end-to-end SNR policy, based on the available channel state information. The results are precise for any number of candidate relays and Rayleigh, Nakagami-m or Rician fading distributions. The effects of different channel fading parameters and the number of relays in the relay selection pool are studied. The system performance is compared to that of dual-hop AF systems without relay selection and to dual-hop AF relaying systems with maximum relay-to-destination SNR relay selection. The adopted selection method provides diversity gain over dual-hop AF relaying systems without relay selection and over maximum relay-to-destination SNR relay selection. The diversity gain is proportional to the relay selection pool size.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".