Exact Error Analysis of Dual-Hop Fixed-Gain AF Relaying over Arbitrary Nakagami-m Fading
Bibliographic record
Abstract
Closed-form error analysis of dual-hop fixed-gain amplify-and-forward (AF) relaying systems for transmissions over Nakagami fading channels has, so far, been tractable only with integer fading parameters. For the general Nakagami-m fading with arbitrary m values, the exact closed-form error analysis is more challenging. This paper goes toward solving this problem by deriving a unified error analysis framework that embraces several general modulation schemes. The obtained Error Probability (EP) expressions involve common functions which can be efficiently evaluated using standard numerical softwares. This work represents a significant improvement on previous contributions, not only by unifying the error probability expressions pertaining to dual-hop fixed gain relaying over integer Nakagami-m fading, but also by extending them to the general arbitrary Nakagami-m fading. Simulation results sustaining our analysis are provided, and the impacts of various parameters on the overall AF relaying system performance are investigated.
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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 | 0.011 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".