Performance Analysis of Multi-Branch Decode-and-Forward Cooperative Diversity Networks over Nakagami-m Fading Channels
Bibliographic record
Abstract
In this paper, the performances analysis of cooperative-diversity networks using adaptive decode-and-forward (DF) relaying over independent non-identical flat Nakagami-m fading channels is investigated. We derive closed-form expressions for the error probability, outage probability and average channel capacity, and analyze their dependence on the channel parameters. In adaptive DF relaying, among M relays that can participate, only C relays (C les M), with good channels to the source, decode and forward (retransmit) the source information to the destination. Then, the destination combines the direct and the indirect signals using maximum ratio combining (MRC) technique. We derive a closed-form expression for the the moment generating function (MGF) of the total signal-to-noise ratio (SNR) at the destination node. Then, we find a closed-form expression for the probability density function (PDF) of the total SNR at the destination. This PDF is used to derive the closed-form expressions of the performance metrics. Computer simulations are used to validate our analytical results. Results show the significant performance improvement due to the use of the adaptive DF cooperative diversity. Also, results indicate that increasing the number of relays will not always decrease the outage probability.
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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 | 0.008 |
| 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 | 0.001 | 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".