Deep Reinforcement Learning for Mobile Traffic Offloading

Project Details


In 5G networks, heterogeneous networks (HetNets) with small cells is a promising architecture to be deployed. The traffic offloading among macro and small cells is inevitably a key issue. Based on the cloud controlling structure, it is possible to design proactive strategies, so operation issues can be predicted and treated before suffering performance degradation. At the same time, the much more complex nature of 5G resource management is happen to be a suitable target to apply advanced machine learning approaches. In the project, we propose to apply deep reinforcement learning (DRL) on energy-efficient mobile traffic offloading. Taking advantage of our traffic forecasting works, we will investigate the DRL model for 5G networking issues, and further provide suggestion for future 5G resource management works.
Effective start/end date1/08/1831/07/19

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 11 - Sustainable Cities and Communities
  • SDG 12 - Responsible Consumption and Production
  • SDG 17 - Partnerships for the Goals


  • 5G
  • Machine Learning
  • Deep Learning
  • Reinforcement Learning
  • HetNets
  • Mobile Traffic Offloading


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