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Network slicing for vehicular communication

Authors: Hamza Khan 0001; Petri Luoto; Sumudu Samarakoon; Mehdi Bennis; Matti Latva-aho;

Network slicing for vehicular communication

Abstract

AbstractUltra‐reliable vehicle‐to‐everything (V2X) communication is essential for enabling the next generation of intelligent vehicles. V2X communication is a growing area of communication that connects vehicles to neighboring vehicles (V2V), infrastructure (V2I), and pedestrians (V2P). Network slicing is one of the promising technologies for connectivity of the next generation devices, creating several logical networks on a common and programmable physical infrastructure. Network slicing offers an efficient way to satisfy the diverse use case requirements by exploiting the benefits of shared physical infrastructure. In this regard, we propose a network slicing‐based communication solution for vehicular networks. In this work, we model a highway scenario with vehicles having heterogeneous traffic demands. The autonomous driving slice (safety messages) and the infotainment slice (video stream) are the two logical slices created on a common infrastructure. We formulated a network clustering and slicing algorithm to partition the vehicles into different clusters and allocate slice leaders (SLs) to each cluster. SLs serve its clustered vehicles with high‐quality V2V links and forwards safety information with low latency. On the other hand, road side unit provides infotainment service using high‐quality V2I links. An extensive Long Term Evolution Advanced system‐level simulator with enhancement of cellular V2X standard is used to evaluate the performance of the proposed method, in which it is shown that the proposed network slicing technique achieves low latency and high‐reliability communication.

Country
Finland
Related Organizations
Keywords

Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
32
Top 10%
Top 10%
Top 10%
Green
bronze