Wireless-Enabled Asynchronous Federated Fourier Neural Network for
Turbulence Prediction in Urban Air Mobility (UAM)
- URL: http://arxiv.org/abs/2201.00626v1
- Date: Sun, 26 Dec 2021 14:41:52 GMT
- Title: Wireless-Enabled Asynchronous Federated Fourier Neural Network for
Turbulence Prediction in Urban Air Mobility (UAM)
- Authors: Tengchan Zeng, Omid Semiari, Walid Saad, Mehdi Bennis
- Abstract summary: 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.
- Score: 101.80862265018033
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT [32.6091251316091]
This paper proposes a UAV-assisted system based on distributed beamforming to enhance age forwarding information (AoI) in Internet of Things (IoT)
We propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing stability and accelerate convergence.
arXiv Detail & Related papers (2025-02-13T07:48:36Z) - Toward Safe Integration of UAM in Terminal Airspace: UAM Route Feasibility Assessment using Probabilistic Aircraft Trajectory Prediction [0.0]
This study proposes a framework to assess the feasibility of Urban Air Mobility (UAM) route integration using probabilistic aircraft trajectory prediction.
The methodology was applied to airspace over Seoul metropolitan area, encompassing interactions between UAM and conventional traffic at multiple altitudes and lanes.
arXiv Detail & Related papers (2025-01-28T00:28:16Z) - UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks [71.23793087286703]
Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications.
This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems.
arXiv Detail & Related papers (2024-12-09T12:56:50Z) - Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach [20.36806314683902]
We study an integrated sensing and communications (ISAC) system for low-altitude economy (LAE)
The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs' trajectories.
We propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique.
arXiv Detail & Related papers (2024-12-05T11:12:46Z) - Over-the-Air Federated Learning and Optimization [52.5188988624998]
We focus on Federated learning (FL) via edge-the-air computation (AirComp)
We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings.
For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error.
In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
arXiv Detail & Related papers (2023-10-16T05:49:28Z) - UAV Swarm-enabled Collaborative Secure Relay Communications with
Time-domain Colluding Eavesdropper [115.56455278813756]
Unmanned aerial vehicles (UAV) as aerial relays are practically appealing for assisting Internet Things (IoT) network.
In this work, we aim to utilize the UAV to assist secure communication between the UAV base station and terminal terminal devices.
arXiv Detail & Related papers (2023-10-03T11:47:01Z) - Deep Learning Aided Routing for Space-Air-Ground Integrated Networks
Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks.
With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links.
We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
arXiv Detail & Related papers (2021-10-28T14:12:10Z) - Learning-Based UAV Trajectory Optimization with Collision Avoidance and
Connectivity Constraints [0.0]
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints.
We propose a decentralized deep reinforcement learning approach to solve the problem.
arXiv Detail & Related papers (2021-04-03T22:22:20Z) - Federated Learning in the Sky: Joint Power Allocation and Scheduling
with UAV Swarms [98.78553146823829]
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks.
In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
arXiv Detail & Related papers (2020-02-19T14:04:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.