Data-Driven Emulation of Mobile Access Networks
Bibliographic record
Abstract
Network monitoring is fundamental to understand network evolution and behavior. However, monitoring studies have the main limitation of running new experiments when the phenomenon under analysis is over e.g., congestion. To overcome this limitation, network emulation is of vital importance for network testing and research experiments either in wired and mobile networks. When it comes to mobile networks, the variety of technical characteristics, coupled with the opaque network configurations, make realistic network emulation a challenging task. In this paper, we address this issue leveraging a large scale dataset composed of 500M network latency measurements in Mobile BroadBand networks. By using this dataset, we create 51 different network latency profiles based on the Mobile BroadBand operator, the radio access technology and signal strength. These profiles are then processed to make them compatible with the tc-netem emulation tool. Finally, we show that, despite the limitation of current tc-netem emulation tool, Generative Adversarial Networks are a promising solution used to create realistic temporal emulation. We believe that this work could be the first step toward a comprehensive data-driven network emulation. For this, we make our profiles and codes available to foster further studies in these directions.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".