On the Application of Network Slicing for 5G-V2X

Ultra-reliable vehicle-to-everything (V2X) communication is essential for enabling the next generation of intelligent vehicles. V2X communication refers to the exchange of information between vehicle and infrastructure (V2I) or between vehicles (V2V). Network slicing is one of the promising technologies for the next generation of connected devices, creating several logical networks on a common and programmable physical infrastructure. Following this idea, we propose a network slicing based communication model for vehicular networks. In this paper, we have modelled a multi-lane highway scenario with vehicles having heterogeneous traffic requirements. Autonomous driving slice (exchanges safety messages) and infotainment slice (provides video stream) are the two logical slices created on a common infrastructure. In addition, a relaying approach is utilized to improve the performance of low signal-to-interferenceplus- noise-ratio (SINR) video streaming vehicles. These low SINR vehicles are served by other infotainment vehicles, which have high quality V2V and V2I link and are not serving as autonomous driving slice access point. An extensive Long Term Evolution Advanced (LTE-A) system level simulator is used to evaluate the performance of the proposed method, in which it is shown that the proposed network slicing approach increases the packet reception ratio (PRR) from 31.15% to 99.47%.

Authors:
Khan Hamza, Luoto Petri, Bennis Mehdi, Latva-aho Matti

Publication type:
A4 Article in conference proceedings

Place of publication:
European Wireless 2018

Keywords:
autonomous driving slice, infotainment slice, LTE-A, system level simulation, URLLC, V2I, V2V

Published:

Full citation:
Khan, H., Luoto, P., Bennis, M. & Latva-aho, M. (2018). On the application of network slicing for 5G-V2X. In European Wireless 2018: 24th European Wireless Conference (pp. 203-208). Berlin: VDE Verlag.

DOI:
https://ieeexplore.ieee.org/abstract/document/8385521

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020101383837