Mobile Network Data Synthesis with Generative AI: Challenges and Solutions

Mobile network data plays a vital role in designing intelligent services for cellular, vehicular, and satellite networks, etc. However, restricted data access poses a barrier to conducting effective and open data-driven research. Data synthesis is a promising solution to address the barrier in the era of generative artificial intelligence (GAI). In this article, we comprehensively study mobile network data synthesis via GAI techniques. Specifically, we first discuss the motivation and challenges for implementing mobile network data synthesis. Then, we introduce several key technologies that address challenges specific to typical mobile network traffic, trajectory, and application usage data. Finally, we propose an AppSyn method for synthesizing mobile application usage data based on large language models and conduct a case study. Experimental results demonstrate the effectiveness of our proposed method compared to state-of-the-art benchmarks.