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A Proposed V2V Path Loss Model: Log-Ray

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Abstract

Vehicle-to-vehicle (V2V) communication that has prominent involvement in the design of innovative communication solutions for the intelligent transportation system. Therefore, to develop V2V channel models, the knowledge of the propagation channel on which comprehensive research efforts have been carried is vital. In this study, we focused on the analysis and modeling of path loss characteristics for V2V communications and proposed a log-ray path loss model with the motivation of overcoming remarkable fitting errors between the two-ray model and measured data where the path loss exponent corresponds to different from 2. First, channel measurements were performed at 5.9 GHz with commercially available DSRC OBU devices for five different scenarios in typical highway and suburban environments in Gümüşhane, Turkey. Then, the large-scale characteristics of different propagation scenarios were analyzed and the proposed log-ray model performance was compared with commonly used log-distance and two-ray path loss models qualitatively and quantitatively. On the other hand, analysis of small-scale modeling and shadowing were also conducted for all scenarios and presented in the results. The findings clearly demonstrate that the proposed log-ray path loss model fits the measured data with a smaller RMSE value (2.54 dB) than the results of the log-distance (3.07 dB) and two-ray (3.72 dB) path loss models in the literature. In other words, the proposed model has approximately 17.5% and 32% increase in fitting performance according to the log-distance and the two-ray path loss models, respectively. The study results will be helpful for better performance analysis and system design of V2V communication.

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Acknowledgements

This work was supported by the Scientific Research Projects Coordination Unit of Karadeniz Technical University. Project number: 7350.

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Correspondence to Kenan Kuzulugil.

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Kuzulugil, K., Tugcu, Z.H. & Cavdar, I.H. A Proposed V2V Path Loss Model: Log-Ray. Arab J Sci Eng 48, 14901–14911 (2023). https://doi.org/10.1007/s13369-023-07922-6

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