  This article proposes a generative neural network architecture for spatially
consistent air-to-ground channel modeling. The approach considers the
trajectories of uncrewed aerial vehicles along typical urban paths, capturing
spatial dependencies within received signal strength (RSS) sequences from
multiple cellular base stations (gNBs). Through the incorporation of
conditioning data, the model accurately discriminates between gNBs and drives
the correlation matrix distance between real and generated sequences to minimal
values. This enables evaluating performance and mobility management metrics
with spatially (and by extension temporally) consistent RSS values, rather than
independent snapshots. For some tasks underpinned by these metrics, say
handovers, consistency is essential.
