Online Truthful Mechanism Design in Wireless Communication Networks
Bibliographic record
Abstract
The recent upsurge of mobile devices such as smartphones and tablets has caused scarcity of network resources, such as spectrum and computation resources. Moreover, mobile devices ordinarily join or leave networks for their own convenience, resulting in high network dynamics. As a result, developing advanced technologies based on the concepts of cooperation, sharing, and reusing becomes necessary for wireless networks. As one of the promising solutions, online truthful mechanism design has been introduced in wireless networks in order to motivate the participation of more mobile users under a dynamic environment. This article first introduces the origination and some basic concepts of the online truthful mechanism, and then provides an exhibition of several online truthful mechanism design methods, which have been widely applied in wireless networks. We primarily present two types of online truthful mechanisms, that is, primal-dual based and time slotted online mechanisms, and then briefly summarize other classes, including Myerson-based, random-based, and matching-based ones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.059 | 0.065 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".