An efficient analytical model for QoS engineering in mobile cellular networks
Bibliographic record
Abstract
This paper presents a simple and accurate analytical model to dimension mobile cellular networks with QoS differentiation. QoS per applicative flow is commonly defined in GPRS/EDGE and 3G systems where streaming applications with real time properties and elastic data applications have to share radio resources. The need for accurate and fast-computing tools is of primary importance to tackle complex and exhaustive dimensioning issues. In this paper, we present a generic QoS modeling methodology developed in the context of EDGE networks but that can be adapted easily to a different technology. We develop a ldquoconditional product-formrdquo Markovian model that takes into account the QoS differentiation between real time and non-real time classes and gives closed form expressions for all the required performance parameters at a click speed. We compare our model with simulation and show its accuracy. The speed of our analytical tool allows tackling dimensioning issues that require several thousands of evaluations, which is not tractable with any simulation tool.
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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 | 0.000 |
| Open science | 0.004 | 0.003 |
| 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; 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".