LAUSR

Random mobility models (RMMs) capture the random mobility patterns of mobile agents, and have been widely used as the modeling framework for the evaluation and design of mobile networks. All existing RMMs in the literature assume independent movements of mobile agents, which does not hold for unmanned aircraft systems (UASs). In particular, UASs must maintain a safe separation distance to avoid collision. In this paper, we propose a new modeling framework of random mobility models equipped with physical sense-and-avoid protocols to capture the flexible, variable, and uncertain movement patterns of UASs subject to separation safety constraints. For the random direction (RD) RMM equipped with a commonly used sense-and-avoid (S&A) protocol, named sense-and-stop (S&S), we provide its statistical properties including stationary location distribution and stationary inter-vehicle distance distribution, using the Markov analysis. This study provides knowledge on the impact of S&A protocols to critical UAS networking statistics. In addition, we define collision probabilities and airspace capacity concepts for UASs based on the inter-vehicle distance distribution, and derive their closed-form expressions. This analytical framework mathematically bridges local autonomy with global airspace capacity, and allows the impact analysis of local autonomy configurations for effective UAS airspace capacity management.