Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

Takashi Koshimizu*, Shi Gengtian, Huan Wang, Zhenni Pan, Jiang Liu, Shigeru Shimamoto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Cellular systems are facing the ever-increasing demand for vehicular communication aimed at applications such as advanced driving assistance and ultimately fully autonomous driving. Cellular Vehicle to Anything (C-V2X) has become more applicable with the release of the first sets of 5G (5th Generation) system specifications. The highly capable 5G systems will therefore support even a larger number of moving objects. This study aims to present a sophisticated clustering mechanism that enables cellular systems to accommodate a massive number of moving Machine Type Communication (MTC) objects with a minimum set of connections while maintaining system scalability. Specifically, we proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) scheme and applied it for Vehicular Ad hoc Network (VANET) clustering. For VANET clustering formation, our study employed Machine Learning (ML) to determine the granularity, i.e., the size and span of clusters desirable for use in dynamic motion environments. The study achieved a sufficient level of prediction accuracy with fewer training data through a learned prediction function based on the selected key criteria. This paper also proposes a system sequence designed with a series of procedures fully compliant with C-V2X systems. We demonstrated substantial simulations and numerical experiments with theoretical analysis, specifically applying soft-margin-based Support Vector Machine (SVM) algorithm. The simulation results confirmed that the granularity parameter we applied fairly controls the size of VANET clusters although vehicles are in motion and that the prediction performance has been adjusted through controlling of key SVM parameters.

Original languageEnglish
Article number9091568
Pages (from-to)94560-94574
Number of pages15
JournalIEEE Access
Publication statusPublished - 2020


  • 5G mobile communication
  • clustering methods
  • heterogeneous networks
  • machine learning
  • machine-to-machine communications
  • mobile computing
  • vehicular ad hoc networks

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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