Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3539
Title: Performance Evaluation of V2V Propagation Channels Under Different Link Types
Authors: Azlan Abd Aziz 
Azwan Mahmud 
Nurul Asyiqin Mamat 
Nur Asyiqin Amir hamzah 
Hadhrami Ab Ghani 
Keywords: geometry-based V2V;propagation channel;link types;vehicle obstruction
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference: 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022 
Abstract: 
Accurate and realistic propagation channel is of paramount requirement in the design of vehicle-to-vehicle (V2V) communications which cover the realistic channel properties and characteristics. Obstructions and propagation link types give significant impact on the communication link and are often ignored in the modelling and analysis of the V2V design and simulation. In this paper, we demonstrate an overview of V2V propagation channel model and wireless channel requirements based on the different link types and obstruction types. We investigate three different environments such as urban, suburban and highway which are in the state of Malacca. We analyze the neighborhood size (defined as the number of vehicles that can communicate with the ego vehicle), received signal power and traffic density under the different environment and link types. The numerical results show that the performance of the V2V communications is directly affected by the link types and the environments. The link quality deterioration is more severe due to the buildings than that due to the vehicles. Majority of the link types are still considered to be line-of-sight (LOS) and this is a promising sign for higher throughput and reliable V2V communication links. This analysis provides insightful planning and modelling foundation in V2V networks.
Description: 
Scopus
URI: http://hdl.handle.net/123456789/3539
ISBN: 978-166546837-4
DOI: 10.1109/IICAIET55139.2022.9936791
Appears in Collections:Faculty of Data Science and Computing - Proceedings

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