Bibliographic record
Abstract
Cooperative diversity has the potential of implementing spatial diversity and mitigating the adverse effects of channel fading without requiring multiple antennas at transmitters and receivers. Traditionally, cooperative diversity is implemented using maximal ratio combining (MRC), where all available nodes relay signals between the source and destination. It has recently been proposed, however, that for each source-destination transmission, only a single best node should be selected to act as a relay. The resulting scheme, referred to as selection cooperation or opportunistic relaying, outperforms MRC schemes and can be implemented in a distributed fashion with limited feedback. This result is not unexpected, as selection requires some (though very limited) information regarding instantaneous channel conditions, while MRC does not. When implemented in a distributed network, however, MRC does require feedback for the synchronization of nodes, rendering a comparison of the two schemes worthwhile and fair. In this chapter, we provide a detailed overview of selection. We begin with a single source-destination pair, and discuss its implementation and performance under various constraints and scenarios. We then discuss a less-common scenario, a multisource network where nodes act both as sources and as relays.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".