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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".