An incentive engineering mechanism for optimizing handoff decisions between cellular data networks and WLANs
Bibliographic record
Abstract
Cellular/WLAN integration has been widely considered to be an economical and effective solution for wireless service providers to provision high bandwidth services to meet the increasing demand for data-oriented applications. Nevertheless, technological and operational differences between these two types of networks result in substantial challenges in the management of cellular/WLAN integration. In this paper, we focus on charging methods for cellular/WLAN integration. We introduce a novel pricing model that takes application characteristics, user profiles and network congestion status into account to dynamically adjust the charging rates of cellular and WLAN services. Based on this model, we develop incentive engineering mechanism that encourages the use of the appropriate networks for specific applications based on the service priority and the current network congestion status. The proposed mechanism dynamically optimizes the charging rate by adapting to changes in networks' and users' behaviors. Numerical results show significant improvements of the system utilization and users' satisfaction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".