SpectraGAN: Spectrum based Generation of City Scale Spatiotemporal Mobile Network Traffic Data

Kai Xu, Rajkarn Singh, Marco Fiore, Mahesh K. Marina, Hakan Bilen, Muhammad Usama, Howard Benn, Cezary Ziemlicki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

City-scale spatiotemporal mobile network traffic data can support numerous applications in and beyond networking. However, operators are very reluctant to share their data, which is curbing innovation and research reproducibility. To remedy this status quo, we propose SpectraGAN, a novel deep generative model that, upon training with real-world network traffic measurements, can produce high-fidelity synthetic mobile traffic data for new, arbitrary sized geographical regions over long periods. To this end, the model only requires publicly available context information about the target region, such as population census data. SpectraGAN is an original conditional GAN design with the defining feature of generating spectra of mobile traffic at all locations of the target region based on their contextual features. Evaluations with mobile traffic measurement datasets collected by different operators in 13 cities across two European countries demonstrate that SpectraGAN can synthesize more dependable traffic than a range of representative baselines from the literature. We also show that synthetic data generated with SpectraGAN yield similar results to that with real data when used in applications like radio access network infrastructure power savings and resource allocation, or dynamic population mapping.
Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2021)
PublisherAssociation for Computing Machinery (ACM)
Pages243–258
ISBN (Electronic)9781450390989
DOIs
Publication statusPublished - 2 Dec 2021
Event17th International Conference on emerging Networking EXperiments and Technologies - Virtual Conference, Munich, Germany
Duration: 7 Dec 202110 Dec 2021
https://conferences2.sigcomm.org/co-next/2021/#!/home

Conference

Conference17th International Conference on emerging Networking EXperiments and Technologies
Abbreviated titleCoNEXT 2021
Country/TerritoryGermany
CityMunich
Period7/12/2110/12/21
Internet address

Keywords / Materials (for Non-textual outputs)

  • Mobile Network Traffic Data
  • Synthetic Data Generation
  • Deep Generative Modeling
  • Conditional GANs

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