Optimal Relay-Subset Selection and Time-Allocation in Decode-and-Forward Cooperative Networks
Bibliographic record
Abstract
We consider a half-duplex mesh network wherein a single source communicates to a destination with the help of N potential decode-and-forward relays. We develop the optimal selection of a relaying subset and allocation of transmission time. This resource allocation is found by maximizing over the rates achievable for each possible subset of active relays; in turn, the optimal time allocation for each subset is obtained by solving a linear system of equations. An assumed relay numbering imposes a causality constraint. We also present a recursive algorithm to solve the optimization problem which reduces the computational load of finding the required matrix inverses and the number of required iterations. We show that (i) optimizing transmission time significantly improves achievable rate; (ii) optimizing over the channel resources ensures that more relays are active over a larger range of signal-to-noise ratios; (iii) linear network constellations significantly outperform grid constellations; (iv) the achievable rate is robust to node ordering.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".