Performance analysis of Decode-and-Forward cooperative networks with best relay selection
Bibliographic record
Abstract
The Decode-and-Forward (DAF) relay strategy is the most popular cooperative diversity scheme due to its performance. Selection DAF (SDAF), relaying single or multiple relay selection, has recently been proven to achieve the same diversity order with lower power consumption than all-participate (AP) networks. Most SDAF methods assume constant power for the best relay, regardless of channel conditions. In the SDAF method, the relay decodes the received signal and re-encodes it to forward it to its destination. We propose a single relay selection strategy for two-hop relay networks. We specify a factor for each relay based on their outage probability criterion and the relay with the lowest probability of outage is selected for further cooperation for DAF scheme. In this paper, we suggest a method to select the best relay, where the destination node decides to cooperate with the relay nodes according to it is own the lowest outage probability for each link between destination-relays nodes. In particular, we derive an expression for Cumulative Distribution Function (CDF) for the total SNR for SDAF. Our simulation results show that this method outperforms AP-DAF method, and can keep a full diversity order.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".