Kinetic Handover Modeling in Nonstationary Cellular Networks with Fluid Spatiotemporal Stochastic Geometry
Abstract: Inferring directional handover traffic from aggregate mobility observations remains challenging because population density evolution does not fully describe the movement of individual users across cell boundaries. This paper develops a kinetic framework for geometry-induced serving-region crossings in nonstationary cellular networks, connecting boundary-normal velocity statistics with fluid spatiotemporal stochastic geometry (F-STSG) for density-based transport reconstruction. The framework distinguishes signed net population transfer from gross bidirectional crossings and reveals two information limits: density evolution does not uniquely determine physical pairwise transport, while first-order transport alone cannot resolve counter-moving crossings. We establish exact recovery conditions for potential transport and develop coherent and Gaussian mobility closures for estimating directional crossing rates. Sharp moment-constrained bounds further characterize the remaining uncertainty under bounded normal velocities without assuming a specific velocity distribution. An error bound links directional estimation accuracy to transport reconstruction and unresolved mobility information. The formulation also recovers the classical stationary Poisson--Voronoi handover law. Numerical experiments examine the information limits, reconstruction sensitivity, and closure accuracy. On 47 held-out vehicular simulation windows, F-STSG achieves lower net-flow reconstruction errors than three baselines, while auxiliary velocity statistics improve directional estimates under matched Gaussian mobility.