Optimal and Fair Rate Adaptation in Wireless Mesh Networks Based on Mathematical Programming and Game Theory
Bibliographic record
Abstract
The authors have proposed a fair solution to optimal rate adaptation. The problem has been described in terms of the objective function, decision variables and constraints. Furthermore, using this problem definition, a rate adaptation heuristic was developed and divided into two sub- problems; part one requires finding the optimal rate allocation within the network, part two continues by finding a fair solution whilst still keeping an optimal rate allocation. The heuristic relies on cooperation in the network, and information regarding selected rates and loss due to interference, is distributed between neighbouring nodes. Furthermore, the heuristic is modelled as a repeated game with infinite horizon and it is shown how cooperation can be enforced. A Stack topology has been used for analysing and comparing performance and OMNeT++ 4.2.2 has been selected as simulation platform. The authors have shown that the heuristic effectively reduces the packet loss ratio (PLR). Thereafter, it was shown that the solution is both fair and optimal in terms of data rate.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".