Comprehensive node selection and power allocation in multi-source cooperative mesh networks
Bibliographic record
Abstract
This paper considers resource allocation with relay selection in a multi-source multi-destination mesh network wherein dedicated relay nodes use the decode-and-forward (DF) protocol. The key difference from previous work is that we consider resource allocation across the source-relay, relay-destination, and source-destination channels in a multi-source network. The solution to the related optimization problem simultaneously solves for relay selection, power allocation, and the cooperation strategy (direct transmission, if optimal, is a valid solution). Since the jointly optimal solution is of exponential complexity, we introduce a set of time-sharing factors and relax the selection constraint, resulting in an upper bound to the true solution. Imposing selection leads to a feasible, but tight, lower bound on the optimal solution. Second, we propose a decentralized selection and power allocation scheme. Simulation results show that the performance of the decentralized selection scheme almost exactly tracks that of the upper bound for both the max-sum and max-min rate metrics while offerring computational benefits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".