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

doi:10.1109/access.2020.2994132

Takashi Koshimizu, Shi Gengtian + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.2994132

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 17	License type: CC BY 4.0

Affiliation: Waseda University

Abstract

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 (5 th 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.

Highlights

The concept of connecting vehicles to anything (V2X) opens a new paradigm in vehicle transportation, leveraging the power of wireless communications [1]
By employing the fundamental concept of Affinity Propagation (AP), this study extended its key decision function in order to find out a scheme highly adaptable to the motion dynamics of Vehicular Ad hoc Network (VANET) in real time
The sub-section explains the details of the task vector and the decision tree we propose, both of which are the essential elements of Support Vector Machine (SVM)

Summary

Introduction

The concept of connecting vehicles to anything (V2X) opens a new paradigm in vehicle transportation, leveraging the power of wireless communications [1]. It is easy to imagine that the way of our daily life will be greatly changed even with realization of one of the potential scenarios, e.g., autonomous driving. For realizing such highly demanded applications and use cases of V2X, aggressive inter-industry discussions have been underway. 5G Automotive Association (5GAA), a newly created cross-industry organization of automotive and telecommunication, has been working on. C-V2X has the capability to provide both shortand long-range communication modes that work interactively to fulfill various scenarios. Scenarios of short-range direct connectivity include vehicle-to-vehicle (V2V), vehicle-toinfrastructure (V2I) and vehicle-to-pedestrian (V2P) communications.

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