Strategic bargaining in wireless networks: basics, opportunities and challenges
Bibliographic record
Abstract
Strategic bargaining cooperative games have found extensive applications to resource management in wireless networks. In this survey, basics of a strategic bargaining game and solution concepts are firstly presented. Geometrical interpretations are introduced to better understand real meanings of them. Then, the authors survey the applications of various strategic bargaining games for the emerging wireless networks, where the authors concentrate on several interesting problems based on their previous systematic studies: (i) distributed resource management design for cognitive radio networks based on geometrical interpretation of the cooperative solution; (ii) asymmetric bargaining modelling for green communications; (iii) a unified utility tradeoff design between spectral and energy efficiency in heterogeneous cellular networks; (iv) the cooperative rate splitting game for Long Term Evolution‐coordinated multi‐point system; and (v) a general bargaining formulation with different tradeoffs between efficiency and fairness. In addition, the authors survey the applications of strategic bargaining games to cooperation incentive mechanism, bargaining game on capacity region of interference channel and multiuser and multimedia applications. Finally, challenges and potential research direction are summarised in this work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".