Reinforcement Learning-Based Network Management based on SON for the 5G Mobile Network

Xizhe Qiu, Chen Yu Chiang, Phone Lin, Shun Ren Yang, Chih Wei Huang

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

Abstract

The 5G heterogeneous network (Het-Net) comprises macro cells and small cells. The small cells with the ultra-dense deployment can offload mobile data traffic from macro cells and extend service area while consuming less energy. However, frequent handoffs between the two types of cells result in high signaling costs and interference. Thus, determining when to switch small cells between active and inactive modes is crucial to reducing operation cost. This paper proposes a Reinforcement Learning-based network management mechanism for 5G HetNet, and simulation experiments were conducted to evaluate its performance, in contrast to previous works that utilized 3GPP standardized Self-Organizing Network (SON) for network management mechanisms.

Original languageEnglish
Title of host publication2023 International Wireless Communications and Mobile Computing, IWCMC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1503-1508
Number of pages6
ISBN (Electronic)9798350333398
DOIs
StatePublished - 2023
Event19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 - Hybrid, Marrakesh, Morocco
Duration: 19 Jun 202323 Jun 2023

Publication series

Name2023 International Wireless Communications and Mobile Computing, IWCMC 2023

Conference

Conference19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023
Country/TerritoryMorocco
CityHybrid, Marrakesh
Period19/06/2323/06/23

Keywords

  • 5G Heterogeneous Network (Het-Net)
  • Network Management
  • Reinforcement Learning (RL)
  • Self-Organizing Network (SON)

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