Uplink QoS-aware admission control in WCDMA networks with class-based power sharing
Bibliographic record
Abstract
Efficient call admission control (CAC) techniques are of paramount importance in UMTS networks to satisfy the quality of service (QoS) requirements of different traffic classes and to utilize the system resources in an efficient manner. In this paper, we propose a novel uplink CAC framework to enhance existing UMTS networks on three related accounts. First, we introduce a measurement-based component to calculate the current load of the system; second, this measurement-based component is integrated with a power prediction module to estimate the load increment that the new call will bring into the system; and third, the proposed framework feeds the results obtained to a call admission control algorithm with a QoS-enforcing mechanism that gives each class of traffic different treatment based on the QoS requirement of the connections. To the best of our knowledge, ours is a first attempt towards combining the above components into one uplink CAC framework that aims to enhance system performance and to achieve per-class QoS objectives. Simulation results show that the framework is able to reduce dropping ratio for active users to zero level. Thus, it satisfies mobile users' needs resulting in stable performance levels during heavy load periods. Furthermore, the framework provides a low blocking ratio for new calls, which translates into high resource utilization. This is a highly desirable property from the service provider point of view.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".