Performance of Decode-and-Forward Cooperative Diversity Networks Over Nakagami-m Fading Channels
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Bibliographic record
Abstract
This paper analyzes the end-to-end bit error rate performance of cooperative diversity networks using decode-and-forward (DF) relaying over independent non-identical flat Nakagami-m fading channels. We derive a closed-form expression for the error rate and analyze its dependence on the channel parameters. In DF cooperative diversity, a relay detects the received signal and then relays the signal to the destination. The destination combines the two signals received from the source and relay. We assume here that the relay decides independently (based on the received signal quality at the relay) whether or not to forward the signal to the destination. Computer simulations are used to validate our analytical results. Results show the significant performance improvement due to the use of the DF cooperative diversity. Also, results indicate that the source-relay channel has the most influence on the error performance.
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it