A game‐theoretic approach for relay assignment over distributed wireless networks
Bibliographic record
Abstract
ABSTRACT For full cooperative diversity gains to be achieved while still maintaining spectral and energy efficiency, relay assignment schemes for cooperative communications have been extensively studied in recent research. These schemes select only the best relay from multiple relaying candidates to cooperate with a communication link. However, it is challenging to find the optimal relay in distributed wireless networks because of the dynamic nature of such networks. In this paper, we first formulate the problem of relay assignment as a noncooperative, mixed‐strategy, repeated game, where relaying candidates are modeled as rational players. We then propose a game‐theory‐based relay assignment schemeGTRA, in which each player plays against all the other players and determines whether to cooperate with a communication link on a packet‐by‐packet basis in a distributed manner. To adapt to dynamic environments, players utilized an adaptive learning algorithm, that is, modified‐regret‐matching algorithm, to learn optimal strategies of relay assignment, as well as to orient the game to converge to a set of correlated equilibriums, which is often more system efficient than a Nash equilibrium. To evaluate the performance ofGTRA, we compare it withBR, a fictitious two‐player game‐based approach. Simulation results have shown thatGTRAoutperformsBRin terms of network throughput, especially in environments where the channel fading becomes severe. It is also shown thatGTRAcan converge to a correlated equilibrium in a short period that enables theGTRAto work well in dynamic environments. Copyright © 2011 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".