Title

Identifying Dsrc Channel Loss Factors Of Urban Intersections Using Rss Datasets

Abstract

Channel modeling is critical for evaluating various protocols designed for different network layers, as credibility of the corresponding evaluation largely depends on the accuracy of the propagation models. Dynamic vehicular network topology and propagation environment define the communication channel behavior. In this paper, we study the received signal strength (RSS) based channel characterization dataset collected from an urban intersection scenario for Dedicated Short Range Communication (DSRC) in the 5.9 GHz band. We conducted a large-scale RSS measurement campaign in one of the densest traffic environments in the US. The channel characterization data was collected using ten DSRC-enabled vehicles in an Orange County, CA intersection environment. Using the dataset, we study and identify the key aspects of the network topology and physical environment that collectively describe the channel behavior and determine realistic wireless propagation channel models for urban intersections. In particular, we show that the path losses are harsh between vehicles on perpendicular roads. Our results may prove to be instrumental in evaluating vehicleto-vehicle (V2V) safety applications.

Publication Date

7-2-2018

Publication Title

IEEE Vehicular Technology Conference

Volume

2018-August

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/VTCFall.2018.8690750

Socpus ID

85064890889 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85064890889

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