1Improving Amplify-and-Forward Relay Networks: Optimal Power Allocation versus Selection
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Bibliographic record
Abstract
We consider an Amplify-and-Forward cooperative diversity system where a source node communicates with a destination node with the help of one or more relay nodes. The conventional system model assumes all relay nodes participate, with the available channel and power resources equally distributed over all nodes. This approach being clearly sub-optimal, we first present two power allocation schemes to minimize the system outage probability, based on complete channel state information and channel statistics, respectively. We further show that the proposed optimal power allocation methods minimize system symbol error rate as well. Next, we propose a selection scheme where only one “best ” relay node is chosen to assist in the transmission. We show that the selection-AF scheme maintains full diversity order, and at reasonable power levels has significantly better outage behavior and average throughput than the conventional all-participate scheme or that with optimal power allocation. Finally we combine power allocation and selection to further improve performance.
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Full frame distilled prediction
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 | 0.000 | 0.000 |
| 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