Joint protocol and relay node selection in collaborative networks
Bibliographic record
Abstract
We consider a multi-hopping communication network of users transmitting information on a shared medium with limited resources, e.g., limited time and limited bandwidth. We assume that two users collaborate (one acts as a relay for another) only if, as a result of the collaboration, they gain more capacity, save on resources or save power.We perform optimal resource allocation for three problems: 1) maximizing the capacity given limited resource and energy, 2) minimizing the resource usage given capacity and energy and 3) minimizing energy usage given capacity and resources. By comparing the collaboration gain as a function of the channels and available energies, one may decide either to collaborate or not and select a collaborator among candidates. We show that a significant gain is attained only if the channel energy gain of one of the users is significantly smaller than those of other involved links. Depending on the channel conditions, there is a given capacity gain ratio that may be obtained, where eta is the environment path loss exponent. We show that the energy minimizing problem is the dual of the capacity maximizing problem. Also we show that users requiring low capacity might not gain more resources from possible collaboration.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".