Service-Oriented Accounting and Charging for 3G and B3G Mobile Environments
Bibliographic record
Abstract
Mobile communication networks need an overall holistic concept for their charging and billing processes, independently of the kinds of services in use. All usage data of a single service should finally be summarized in a single service-oriented complete bill, which shows the individual components of a service separately. In the case of handover among different access networks at the application or the network level, various costs have to be added depending on the kinds of transport networks. In the final bill, all the individual cost factors should be shown separately. For all the concepts which are to be developed, both pre-paid and post-paid payments need to be considered. The existing charging concepts are based on distributed and, typically, uncoordinated collections of charging data. These gathered charging data are aggregated by special charging processes into service-oriented data sets. In addition to the high complexity of the method, it is difficult to distribute service identities, which can be deployed by all network layers for identifying their charging data collection. The main related problems are missing correlation identifiers and the large number of datasets. The charging data collected in a distributed manner must then be combined according to these service identities. It should be pointed out that there are no ready solutions to the problem described above, although partial mechanisms available in the IETF (e.g. RFC 3334 [3]) or in the 3GPP (e.g. 3GPP, TR 32.815 [4]) can certainly be utilized as building blocks. An overview to the IETF architectures is provided by Pras et al. [5]. Koutsopoulou et al. [1] discuss a charging system called CAB. This system reflects the typical centralized accounting and charging architecture used in 2G and 2.5G networks. In the framework of the “3G Evolving Technologies” (3GET) project1, which aims at supporting large-scale and sustained success of the 3G and beyond 3G (B3G) mobile networks, the related issue is tackled from the view point of “Service-Oriented and Convergent Charging”. The presented architecture was built for 3G and B3G networks. Therefore, it might partially fit to 2G and 2.5G networks but we did not concentrate on verifying this applicability. In the context pf this paper, we define accounting as the collection of data about resource consumption. This includes the control of data gathering (via metering), transport and storage of accounting data. Charging derives non-monetary costs for accounting data sets based on service and customer specific tariff parameters. Billing translates costs calculated by the Charging into monetary units and generates a final bill for the customer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".