Network Traffic Load Balance Using Fuzzy C-Mean Clustering in 6G Cellular Networks

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

Abstract

The 6G networks will take all capabilities of 5G networks and add new applications such as Augmented Reality (AR) and Virtual Reality (VR) streaming, ehealth, automated vehicles, smart factories, smart cities and so on. The current cellular networks face the exploding network traffics due to the increasing number of high bandwidth applications. Since mobile users require different network resources and services, the mobile operators are troubled about the network traffic load balancing in their own networks. Thus, the network traffic load balance will be a key challenge to design and deploy 6G networks. The network traffic load balancing is about network traffic load re-distribution by detecting the imbalance of the network traffic load and changing the network parameters. Thus, it is helpful for preventing unexpected network traffics and maximizing the efficiency of the network resources. In this paper, we investigate the network traffic load balance problem and propose a new solution using Fuzzy C Mean (FCM) clustering. The network traffic load balance problem is formulated as a MINLP problem. Using the proposed network traffic load balance method, we obtained maximum 22% performance improvement under the given simulation configuration.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Fuzzy Systems, FUZZ 2023
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)9798350332285
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Fuzzy Systems, FUZZ 2023 - Incheon, Korea, Republic of
Duration: 13 Aug 202317 Aug 2023

Publication series

SeriesIEEE International Conference on Fuzzy Systems
ISSN1098-7584

Conference

ConferenceIEEE International Conference on Fuzzy Systems, FUZZ 2023
Country/TerritoryKorea, Republic of
CityIncheon
Period13/08/2317/08/23

Funding

ACKNOWLEDGEMENT This work was supported by the European Commission in the framework of the H2020-ICT-19-2019 project 5G-HEART (Grant agreement no. 857034).

Keywords

  • 6G
  • etc
  • Fuzzy C mean clustering
  • Handover
  • Machine learning
  • Mixed-integer nonlinear programming
  • Network traffic load balancing method

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