  To meet the growing mobility needs in intra-city transportation, the concept
of urban air mobility (UAM) has been proposed in which vertical takeoff and
landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM,
aircraft can operate in designated air spaces known as corridors, that link the
aerodromes. A reliable communication network between GBSs and aircraft enables
UAM to adequately utilize the airspace and create a fast, efficient, and safe
transportation system. In this paper, to characterize the wireless connectivity
performance for UAM, a spatial model is proposed. For this setup, the
distribution of the distance between an arbitrarily selected GBS and its
associated aircraft and the Laplace transform of the interference experienced
by the GBS are derived. Using these results, the signal-to-interference ratio
(SIR)-based connectivity probability is determined to capture the connectivity
performance of the UAM aircraft-to-ground communication network. Then,
leveraging these connectivity results, a wireless-enabled asynchronous
federated learning (AFL) framework that uses a Fourier neural network is
proposed to tackle the challenging problem of turbulence prediction during UAM
operations. For this AFL scheme, a staleness-aware global aggregation scheme is
introduced to expedite the convergence to the optimal turbulence prediction
model used by UAM aircraft. Simulation results validate the theoretical
derivations for the UAM wireless connectivity. The results also demonstrate
that the proposed AFL framework converges to the optimal turbulence prediction
model faster than the synchronous federated learning baselines and a
staleness-free AFL approach. Furthermore, the results characterize the
performance of wireless connectivity and convergence of the aircraft's
turbulence model under different parameter settings, offering useful UAM design
guidelines.
