Medium Access Probability Model Based on CSMA/CA for a DSRC Network Driven by Poisson Line Process

Vehicular networks are gaining a huge momentum as a mean for an intelligent transportation system that can address numerous problems related to traffic monitoring, accidents, autonomous driving, etc. However, the deployment of vehicular networks is a big challenge for vehicles manufacturers and governments in terms of responsibility and cost. Compared to related research efforts that assume a fixed medium access probability (MAP) of nodes, this paper presents an analytical model using the stochastic geometry tool where we derive the MAP of DSRC nodes in a network composed of vehicles (DSRC nodes) that are located on a system of roads. We then model the roads using a Poisson line process (PLP) and the locations of nodes on each road as a homogeneous 1D Poisson point process (PPP). Also, we model the MAP based on the IEEE 802. 11p standard, where we consider the effect of interference and the carrier sense multiple access with collision avoidance (CSMA/CA). We validated the analytical model against Monte Carlo simulations to prove its accuracy and reliability. The importance of our work stems from the fact that the developed MAP model contributes to the understanding of future DSRC network deployments.

[1]  François Baccelli,et al.  Poisson Cox Point Processes for Vehicular Networks , 2018, IEEE Transactions on Vehicular Technology.

[2]  Martin Haenggi,et al.  Stochastic Geometry Analysis of Cellular Networks , 2018 .

[3]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[4]  Abdel-karim Ajami,et al.  Analyzing the Impact of the Coexistence With IEEE 802.11ax Wi-Fi on the Performance of DSRC Using Stochastic Geometry Modeling , 2019, IEEE Transactions on Communications.

[5]  A. M. Abdullah,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1997 .

[6]  Martin Haenggi,et al.  Stochastic Geometry for Wireless Networks , 2012 .

[7]  Jeffrey G. Andrews,et al.  A Tractable Approach to Coverage and Rate in Cellular Networks , 2010, IEEE Transactions on Communications.

[8]  Juan-Carlos Cano,et al.  Emergency Services in Future Intelligent Transportation Systems Based on Vehicular Communication Networks , 2010, IEEE Intelligent Transportation Systems Magazine.

[9]  Eylem Ekici,et al.  Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions , 2011, IEEE Communications Surveys & Tutorials.

[10]  Ning Wang,et al.  Spatial Point Process Modeling of Vehicles in Large and Small Cities , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[11]  Jeffrey G. Andrews,et al.  Modeling and Analyzing the Coexistence of Wi-Fi and LTE in Unlicensed Spectrum , 2015, IEEE Transactions on Wireless Communications.

[12]  Hassan Artail,et al.  Fairness in future Licensed Assisted Access (LAA) LTE networks: What happens when operators have different channel access priorities? , 2017, 2017 IEEE International Conference on Communications Workshops (ICC Workshops).

[13]  T. Mattfeldt.  Stochastic Geometry and Its Applications , 1996 .

[14]  Abdel-karim Ajami,et al.  On The Modeling and Analysis of Uplink and Downlink IEEE 802.11ax Wi-Fi With LTE in Unlicensed Spectrum , 2017, IEEE Transactions on Wireless Communications.

[15]  François Baccelli,et al.  A Stochastic Geometry Analysis of Dense IEEE 802.11 Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[16]  Harpreet S. Dhillon,et al.  Coverage Analysis of a Vehicular Network Modeled as Cox Process Driven by Poisson Line Process , 2017, IEEE Transactions on Wireless Communications.