Performance Analysis of Decode-and-Forward Cooperative Diversity Using Differential EGC over Nakagami-m Fading Channels
Bibliographic record
Abstract
Cooperative diversity is a promising technology for future wireless networks. In this paper, we derive the average bit error rate (BER) and outage probability (Pout) for differential equal gain combining (EGC) in cooperative diversity networks. The considered network uses adaptive decode-and-forward (DF) relaying over independent non-identical Nakagami-m fading channels. In adaptive DF relaying among M relays, that can participate, only C relays (C les M), with good channels to the source, decode and then forward (retransmit) the source information to the destination. Then, the destination combines the direct and the indirect signals using differential EGC. We first derive a simple exact expression for the equivalent SNR at the destination. Second, we derive the expressions of the PDF and the MGF of this equivalent total SNR at the destination. Then the MGF is used to determine the error and outage probabilities of adaptive DF with an arbitrary number of relays. Furthermore, we found (in terms of MGF) the SNR moments, the average signal-to-noise ratio (SNR) and the amount of fading. 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 show that the performance of the adaptive DF differential EGC is comparable to the adaptive DF maximum ratio combining (MRC) 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".