English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To meet the growing mobility needs in intra-city transportation, the conceptof urban air mobility (UAM) has been proposed in which vertical takeoff andlanding (VTOL) aircraft are used to provide a ride-hailing service. In UAM,aircraft can operate in designated air spaces known as corridors, that link theaerodromes. A reliable communication network between GBSs and aircraft enablesUAM to adequately utilize the airspace and create a fast, efficient, and safetransportation system. In this paper, to characterize the wireless connectivityperformance for UAM, a spatial model is proposed. For this setup, thedistribution of the distance between an arbitrarily selected GBS and itsassociated aircraft and the Laplace transform of the interference experiencedby the GBS are derived. Using these results, the signal-to-interference ratio(SIR)-based connectivity probability is determined to capture the connectivityperformance of the UAM aircraft-to-ground communication network. Then,leveraging these connectivity results, a wireless-enabled asynchronousfederated learning (AFL) framework that uses a Fourier neural network isproposed to tackle the challenging problem of turbulence prediction during UAMoperations. For this AFL scheme, a staleness-aware global aggregation scheme isintroduced to expedite the convergence to the optimal turbulence predictionmodel used by UAM aircraft. Simulation results validate the theoreticalderivations for the UAM wireless connectivity. The results also demonstratethat the proposed AFL framework converges to the optimal turbulence predictionmodel faster than the synchronous federated learning baselines and astaleness-free AFL approach. Furthermore, the results characterize theperformance of wireless connectivity and convergence of the aircraft'sturbulence model under different parameter settings, offering useful UAM designguidelines.

Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)