AppShot: A Conditional Deep Generative Model for Synthesizing Service-Level Mobile Traffic Snapshots at City Scale

Chuanhao Sun, Kai Xu, Marco Fiore, Mahesh K. Marina, Yue Wang, Cezary Ziemlicki

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Service-level mobile traffic data enables research studies and innovative applications with a potential to shape future service-oriented communication systems and beyond. However, real-world datasets reporting measurements at the individual service level are hard to access as such data is deemed commercially sensitive by operators. AppShot is a model for generating synthetic high-fidelity city-scale snapshots of service level mobile traffic. It can operate in any geographical region and relies solely on easily available spatial context information such as population density, thus allowing the generation of new and open traffic datasets for the research community. The design of AppShot is informed by an original characterization of service-level mobile traffic data. AppShot is a novel conditional GAN design instantiated by a convolutional neural network generator and two discriminators. The model features several other innovative mechanisms including multi-channel and overlapping patch based generation to address the unique challenges involved in generating mobile service traffic snapshots. Experiments with ground-truth data collected by a major European operator in multiple metropolitan areas show that AppShot can produce realistic network loads at the service level for areas where it has no prior traffic knowledge, and that such data can reliably support service-oriented networking studies.
Original languageEnglish
Pages (from-to)4136-4150
Number of pages15
JournalIEEE Transactions on Network and Service Management
Issue number4
Early online date17 Aug 2022
Publication statusPublished - 1 Dec 2022

Keywords / Materials (for Non-textual outputs)

  • Mobile network traffic data
  • mobile services
  • synthetic data generation
  • deep generative models
  • generative adversarial networks
  • mobile traffic analysis
  • mobile network resource management
  • network slicing


Dive into the research topics of 'AppShot: A Conditional Deep Generative Model for Synthesizing Service-Level Mobile Traffic Snapshots at City Scale'. Together they form a unique fingerprint.

Cite this