A Synthetic User Behavior Dataset Design for Data-Driven AI-Based Personalized Wireless Networks

Abstract

It is envisioned that wireless networks of the future will support personalized, fine-grained services and decisions by predicting user satisfaction in real-time using machine learning and big data analytics. Data-driven personalization will empower wireless networks to further optimize resources while maintaining user expectations of networks. In order to design, test, and validate research ideas related to wireless network personalization, acquiring data is essential. However, datasets that comprise user behavior and corresponding user satisfaction information are generally not published due to privacy and confidentiality concerns. To account for this, in this paper, we propose a synthetic dataset design methodology to generate labeled user behavior data with ground truth satisfaction values which mimic the real characteristics of real datasets. Finally, we conduct sample user satisfaction prediction experiments using several machine learning algorithms.