Dataset Modeling for Data-Driven AI-Based Personalized Wireless Networks
Bibliographic record
Abstract
Current wireless networks are over-provisioned in order to maintain an average acceptable user experience for most users on the network. Over-provisioned networks suffer from several issues, however, including network inefficiency and the inability to maintain a certain user satisfaction level for all users. Data-driven wireless network personalization is proposed as a dynamic context-aware approach to maintaining the targeted personalized satisfaction levels with minimum resources. Wireless network personalization has two key enablers: measuring and predicting user satisfaction in real-time, and datasets that have both context and user satisfaction information. In this paper, we first present the Zone of Tolerance (ZoT) concept, which is proposed for modeling the relationship between context, service performance, and user satisfaction. Then, since datasets for user behavior and their corresponding satisfaction levels do not exist due to privacy and confidentiality concerns, we propose a process based on the ZoT model for synthesizing a context-based dataset along with its corresponding user satisfaction values. Finally, an exemplary user satisfaction prediction experiment is conducted with the generated dataset using several Machine Learning (ML) algorithms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".