DDPG-based radio resource management for user interactive mobile edge networks

Po Chen Chen, Yen Chen Chen, Wei Hsiang Huang, Chih Wei Huang, Olav Tirkkonen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


The development of the fifth-generation (5G) system on capability and flexibility enables emerging applications with stringent requirements, such as ultra-high-resolution video streaming and online interactive virtual reality (VR) gaming. Hence, the resource management problem becomes more complicated than in the past, and machine learning can be a powerful tool to provide solutions. In this article, the Deep Deterministic Policy Gradient (DDPG) is used to schedule resources in an edge network environment. We integrate a 3D radio resource structure with componentized Markov decision process (MDP) actions to work on user interactivity-based groups. From the simulation results, we can see that more users are satisfied with DDPG-based radio resource management, especially in bandwidth and latency demanding situations.

Original languageEnglish
Title of host publication2nd 6G Wireless Summit 2020
Subtitle of host publicationGain Edge for the 6G Era, 6G SUMMIT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728160474
StatePublished - Mar 2020
Event2nd 6G Wireless Summit, 6G SUMMIT 2020 - Levi, Lapland, Finland
Duration: 17 Mar 202020 Mar 2020

Publication series

Name2nd 6G Wireless Summit 2020: Gain Edge for the 6G Era, 6G SUMMIT 2020


Conference2nd 6G Wireless Summit, 6G SUMMIT 2020
CityLevi, Lapland


  • Deep Deterministic Policy Gradient (DDPG)
  • Machine learning
  • Mobile edge network
  • Radio resource management
  • Reinforcement learning
  • Virtual reality


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