Utility function design for strategic radio resource management games: An overview, taxonomy, and research challenges
Bibliographic record
Abstract
Abstract Radio resource management is important for wireless communication networks. Game theory has been extensively used to model, analyze, and design interactive behaviors and the strategic decision‐making for radio resource management. It is known that utility function is one of the critical elements in a game, which characterizes the preferred relationship of the rational players and is a function of the action of players and their opponents. We first overview the basics of game theory and utility functions. We then present a taxonomy of utility functions with respect to different types of players, the nature of actions, and preferences in terms of the fairness, quality of service, and quality of experience. We provide some insights based on the taxonomy of utility functions, which provides the readers with a comprehensive view. Following that, we also discuss other types of traffic‐aware utility functions with different fairness and the potential and super modular game‐theoretic utility functions. In addition, we summarize the desired properties and observations for the design of suitable utility functions. Finally, we investigate impacts of the pricing in utility functions. This article ends with the conclusions and a promising view on open problems and challenges with possible future research directions.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".