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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".