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20182026
most citedModel-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks

14 citations · 17 across the 18 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

eess.SY2022

Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations

Mohammadsaleh Nikooroo, Omid Esrafilian, Zdenek Becvar +1

Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of…

eess.SP2022

QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs

Mohammadsaleh Nikooroo, Zdenek Becvar, Omid Esrafilian +1

The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, s…

cs.IT2022

UAV-Aided Multi-Community Federated Learning

Mohamad Mestoukirdi, Omid Esrafilian, David Gesbert +1

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communitie…

cs.IT2022

UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks

David Gesbert, Omid Esrafilian, Junting Chen +2

The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet s…

cs.IT2022

UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models

Omid Esrafilian, Rajeev Gangula, David Gesbert

This paper considers the problem of ground user localization based on received signal strength (RSS) measurements obtained by an unmanned aerial vehicle (UAV). We treat UAV-user li…