Perception Messages in Vehicular Networks: A Joint Analysis of Transmission Cost, Peak AoI, and Autocorrelation of Message Size

This paper addresses the optimization of the perception message periodicity in vehicular networks by minimizing a weighted sum of transmission and freshness costs. The expressions of the two contributions and the closed-form solution to the problem are obtained for the Dynamic Scheme of the New Radio Vehicle-to-Everything Side Link standard. Moreover, a model rooted in queueing theory is proposed to describe a connected automated vehicle as a source of messages about the detected objects. The model provides the statistical description of the number of objects in the vehicle viewing horizon, wherefrom the expression of its autocorrelation function is determined. Through the latter, the degree of similarity between the content of consecutive messages is understood. Lastly, an illustrative suburban scenario is investigated and realistically implemented in a simulator. The results show that increasing the detection range of the connected automated vehicle is beneficial, allowing higher values of the optimal update periodicity, larger message sizes, and lower correlation levels between consecutive messages. This choice leads to the best communication performance, guaranteeing a higher probability of successful packet delivery.