Relay Selection Based on Bayesian Decision Theory in Cooperative Wireless Networks
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Bibliographic record
Abstract
Wireless networks use relay nodes as cooperative nodes to gain maximum diversity. Relay selection is one of the key challenging problems in multiuser wireless cooperative networks. This paper addresses the selection problem of the relay node and proposes posterior probability-based relay node selection methods. In these methods, all calculations are derived by either source or destination, consider both amplify-forward and decode-forward methods, and apply Bayesian decision theory to select the relay node. In the source-based method, each source node considers all the relay nodes' channel information to estimate posterior probability using Bayes theorem, whereas in the destination-based method, the destination node considers all source node channel information to calculate posterior probability. Numerical results show that our proposed relay assignment methods maximize the overall data rate of the networks and work well independently of the number of relay nodes or source-destination pairs in the network.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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