The Bottleneck Effect of Rician Fading in Dissimilar Dual-Hop AF Relaying Systems
Bibliographic record
Abstract
New infinite-series expressions are obtained for the probability density function (PDF) and the cumulative distribution function (CDF) of the instantaneous end-to-end signal-to-noise ratio (SNR) of dual-hop amplify-and-forward (AF) relaying systems operating over Rician fading channels and dissimilar dual-hop AF systems operating over mixed Nakagami-m/Rician fading channels. Precise analytical solutions for outage probability, as well as precise single-integral solutions for the ergodic capacity and the average symbol error probability, are obtained. Simulation results are used to verify the solutions obtained. It is shown that the limiting slopes of the average symbol error probability curves and the outage probability curves are not affected by the Rician fading parameter in contrast to the case of Nakagami-m fading links. For the case of mixed fading links, the exact performance metrics are compared to performance bounds in the literature. It is shown that the existing performance bounds are not tight for medium ranges of SNR. The effects of the Rician and Nakagami-m fading parameters on the system performance are also studied. It is shown that, while increasing the Rician parameter results in a notable SNR gain, increasing the Nakagami-m parameter results in a negligible improvement in the system performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".