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 scheme GTRA , 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 of GTRA , we compare it with BR , a fictitious two‐player game‐based approach. Simulation results have shown that GTRA outperforms BR in terms of network throughput, especially in environments where the channel fading becomes severe. It is also shown that GTRA can converge to a correlated equilibrium in a short period that enables the GTRA to work well in dynamic environments. Copyright © 2011 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".