Modeling and Analysis of Sensing Assisted UAV Networks for Urban Vehicular Communications

Abstract

Urban vehicular networks (VNs) demand seamless connectivity and situational awareness within road-constrained environments, motivating the deployment of unmanned aerial vehicles (UAVs) platforms capable of simultaneously sensing vehicles and establishing communication with them. In this paper, we present a sensing-assisted UAV network that provides connectivity to the vehicles in an urban area. The road network of the urban area is modeled as Manhattan Poisson line process (MPLP), and the random location of vehicles on each road is modeled as one dimensional Poisson point processes (PPPs). UAVs are distributed in the urban area at a fixed altitude and provide connectivity after sensing the vehicles. Their locations are modeled as a two-dimensional homogeneous PPP. Combined with the fixed altitude, this results in a three-dimensional spatial configuration. We incorporate an elevation dependent blockage model and define the sensing radius based on detection probability (DP), showing that it is jointly limited by signal strength and blockage effects. We derive the DP and characterize the typical UAV's sensing region within the reliability requirements. We also derive the Laplace transform (LT) of aggregate interference accounting for directional patterns and sensing-driven activity, and analyze the resulting coverage probability (CP). Finally, we obtain the rate coverage (RC) of sensed vehicles falling within the UAV's sensing zone. Numerical results shows that increasing altitude degrades sensing and coverage performance, whereas RC exhibits a non-monotonic trend, first decreasing and then increasing with altitude.