Optimal distributed interference avoidance: potential game and learning
Bibliographic record
Abstract
ABSTRACT This article studies the problem of distributed interference avoidance (IA) through channel selection for distributed wireless networks, where mutual interference only occurs among nearby users. First, an interference graph is used to characterise the limited range of interference, and then the distributed IA problem is formulated as a graph colouring problem. Because solving the graph colouring problem is non‐deterministic polynomial hard even in a centralised manner, the task of obtaining the optimal channel selection profile distributively is challenging. We formulate this problem as a channel selection game, which is proved to be an exact potential game with the weighted aggregate interference serving as the potential function. On the basis of this, a distributed learning algorithm is proposed to achieve the optimal channel selection profile that constitutes an optimal Nash equilibrium point of the channel selection game. The proposed learning algorithm is fully distributed because it needs information about neither the network topology nor the actions and the experienced interference of others. Simulation results show that the proposed potential game theoretic IA algorithm outperforms the existing algorithm because it minimises the aggregate weighted interference and achieves higher network rate. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".