Game Theoretic Approaches for Multiple Access in Wireless Networks: A Survey
Bibliographic record
Abstract
Multiple access methods in a wireless network allow multiple nodes to share a set of available channels for data transmission. The nodes can either compete or cooperate with each other to access the channel(s) so that either an individual or a group objective can be achieved. Game theory, which is a mathematical tool developed to understand the interaction among rational entities, can be applied to model and to analyze individual or group behaviour of nodes for multiple access in wireless networks. Game theory also enables us to model the selfish/malicious behaviour of nodes, and subsequently design the punishment or defense mechanisms for robust multiple access in wireless networks. In addition, game models can provide distributed solutions to the multiple access problems, which are based on solid theoretical foundations. In this survey, we provide a comprehensive review of the game models (e.g., noncooperative/cooperative, static/dynamic, and complete/incomplete information) developed for different multiple access schemes (i.e., contention-free and contention-based random channel access) in wireless networks. We consider time-division multiple access (TDMA), frequency-division multiple access (FDMA), and code-division multiple access (CDMA), ALOHA, and carrier sense multiple access (CSMA)-based wireless networks. In addition, game models for multiple access in dynamic spectrum access-based cognitive radio networks are reviewed. The major findings from the game models used for these different access schemes are highlighted. To this end, several of the key open research directions are outlined.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".