Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Yulei Wu*, Yuxiang Ma, Hong Ning Dai, Hao Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

31 Citations (Scopus)


5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing systems since the introduction of 5G, but also includes a range of autonomous moving platforms, e.g., autonomous flying vehicles, autonomous underwater vehicles, autonomous surface vehicles as well as autonomous land vehicles. These platforms can be used as an effective means to connect air, space, ground, and sea mobile networks for providing a wider diversity of Internet services. Deep learning has been widely used to extract useful information from network big data for enhancing network quality-of-service and user quality-of-experience. Privacy preservation for user and network data is a burning concern in 5G heterogeneous networks due to various attacks in this environment. In this paper, we conduct an in-depth investigation on how deep learning can cope with privacy preservation issues in 5G heterogeneous networks, in terms of heterogeneous radio access networks (RANs), beyond-RAN networks, and end-to-end network slices, followed by a set of key research challenges and open issues that aim to guide future research.

Original languageEnglish
Article number107743
JournalComputer Networks
Early online date15 Dec 2020
Publication statusPublished - 11 Feb 2021
Externally publishedYes

Scopus Subject Areas

  • Computer Networks and Communications

User-Defined Keywords

  • 5G
  • Deep learning
  • Heterogeneous networks
  • Network slicing
  • Privacy preservation


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