Characterizing and modeling user mobility in a cellular data network
Why this work is in the frame
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Bibliographic record
Abstract
The demand for cellular data networks is expected to increase with 3G and beyond technologies accompanied by high-bandwidth consumer services, such as wireless video and camera phones. User mobility affects quality of service, and makes capacity planning more difficult. This paper presents an analysis of user mobility patterns based on data traffic traces from a major regional CDMA2000 cellular network. We find low overall mobility in the network, power-law characteristics in user mobility profiles, and weak correlations between call activity and mobility levels for individual users. We also find that users concentrate their activity in a "home cell" with frequent shorter trips to other locations in the network. Based on the empirical findings, we develop and parameterize a model of cellular data user mobility and show its practical use in simulation.
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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 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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 it