Framework for Vertical Performance Assessment in Very Large-Scale Cellular Networks
Bibliographic record
Abstract
As 4G networks evolve toward full 5G deployment and beyond 5G (B5G) research begins, new vertical opportunities are being introduced in the context of intelligent transportation, power systems, smart-city urban operation, security services, etc. The diversity of the vertical requirements as well as their particular implementation in citywide locations will make it a real challenge to be able to assess or predict application performance for some of those verticals. In this article, we were motivated by the fact that because of the very nature of verticals and their interaction with a very large-scale telecommunications infrastructure, neither measurements nor small-scale simulations will be sufficient for application performance monitoring and prediction. To enable the assessment and prediction of verticals, so-called Quality of Experience (QoE), we propose a comprehensive framework for very large-scale simulation. The framework is based on the random generation of realistic key performance indicators (KPIs), which vary according to changes produced by the dynamics of urban systems. The probability distributions used for context-dependent random generation are built from samples taken from either “traditional” small-scale simulations or real local measurements. Finally, we present the framework’s key features and current roadblocks for an end-to-end widespread implementation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".