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 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 | 0.000 | 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 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".