Performance Analysis of Dual-Hop Relaying Communications Over Generalized Gamma Fading Channels
Bibliographic record
Abstract
In this paper, we present closed form expressions for tight lower bounds of the performance of dual-hop non- regenerative relaying over independent non-identical generalized gamma fading channels. The generalized gamma distribution is very versatile and accurately approximates many of the commonly used channel models for multi-path, shadow, and composite fading. Since it is hard to find a closed form expression for the probability density function (PDF) of the signal-to-noise ratio (SNR) for the generalized gamma distribution, we use an approximate value instead. Novel closed form expressions for the PDF, outage probability and the moments of the approximate value of the SNR at the destination are derived. Also, the average SNR and amount of fading are determined. Moreover, closed form expressions (in terms of the tabulated Meijer's G-function) are found for the average symbol error probability (for several modulations schemes) as well as the Shannon capacity. It should be noted that the Meijer's G-function is widely available in many scientific software packages, such as MATHEMATICAreg and MAPLEreg. Finally, simulations results are also shown 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".