A comprehensive 3-dimensional random mobility modeling framework for airborne networks

To design and evaluate airborne networks (ANs), it is crucial to utilize random mobility models (RMMs) that capture the physical movement patterns of different aerial vehicles in real scenarios. Compared to expensive flight field tests, RMM-based modeling, simulation, and emulation is cost-effective with a large set of RMM-generated flight trajectories. Despite the importance of RMMs, we notice that most existing models focus on the 2-D movement, and do not consider the temporal and 3-D spatial correlation of aerial mobility patterns. In this paper, we propose a comprehensive 3-D smooth turn (ST) modeling framework for fixed-wing aircraft, which can serve as a design and evaluation foundation for future ANs. In the proposed framework, we develop two realistic 3-D ST RMMs that capture the diverse mobility patterns of fixed-wing aircraft, through coupling stochastic forcing with physical laws that govern the 3-D aerial maneuvers. We also develop two boundary models to determine the movement of aerial vehicles when they approach simulation boundaries. Moreover, we propose an approach to estimate the optimal 3-D ST RMMs, with which we can produce rich trajectory ensembles with statistical mobility patterns that match with the real trajectory data.

To design and evaluate airborne networks (ANs), it is crucial to utilize random mobility models (RMMs) that capture the physical movement patterns of different aerial vehicles in real scenarios. Compared to expensive flight field tests, RMM-based modeling, simulation, and emulation is cost-effective with a large set of RMM-generated flight trajectories. Despite the importance of RMMs, we notice that most existing models focus on the 2-D movement, and do not consider the temporal and 3-D spatial correlation of aerial mobility patterns. In this paper, we propose a comprehensive 3-D smooth turn (ST) modeling framework for fixed-wing aircraft, which can serve as a design and evaluation foundation for future ANs. In the proposed framework, we develop two realistic 3-D ST RMMs that capture the diverse mobility patterns of fixed-wing aircraft, through coupling stochastic forcing with physical laws that govern the 3-D aerial maneuvers. We also develop two boundary models to determine the movement of aerial vehicles when they approach simulation boundaries. Moreover, we propose an approach to estimate the optimal 3-D ST RMMs, with which we can produce rich trajectory ensembles with statistical mobility patterns that match with the real trajectory data.