Subscription Policy Control Framework for IMS-Based Networks
Bibliographic record
Abstract
The Policy and Charging Control (PCC) architecture was firstly introduced in the 3GPP’s Release 7. However, the PCC has its problems. The main problems include the incapability of performing policy control with consideration of subscriber profiles and missing specification on how to organize and express the policy information. In addition, no policy control at application session establishment stage also contributes to its imperfectness. In this paper, the authors propose a subscription-based policy control framework that implements a subscription-centered approach for policy control and to enable flexible policy definitions based on the subscriber’s profile at the application level. The framework also provides functionalities of organizing the subscription data, identifying the policy, regulating the policy control process, interpreting, managing and enforcing the corresponding policies. The main objective is to qualify the subscribers and thus, enhance the network customization through defining flexible policies based on policy control requirements for different subscribers.
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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 | 0.002 | 0.000 |
| 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".