Maximizing Throughput of Linear Vehicular Ad-hoc NETworks (VANETs) - a Stochastic Approach

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In this paper we use stochastic geometry to propose two models for Aloha-based linear VANETs. The first one uses Signal over Interference plus Noise Ratio (SINR) capture condition to qualify a successful transmission, while the second one expresses the transmission throughput as a function of SINR using Shannon’s law. Assuming Poisson distribution of vehicles, power-law mean path-loss and Rayleigh fading, in these models we derive explicit formulas for the mean throughput and the probability of a successful reception at a given distance. Furthermore, we optimize two quantities directly linked to the achievable network throughput: the mean density of packet progress and the mean density of information transport. This is realized by tuning the communication range and the probability of channel access. We also present numerical examples and study the impact of external noise on an optimal tuning of network parameters.