most citedFederated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP20201 cited

Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning

Behzad Khamidehi, Elvino S. Sousa

To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many appl…

cs.NI2020

5G is Real: Evaluating the Compliance of the 3GPP 5G New Radio System with the ITU IMT-2020 Requirements

Samer Henry, Ahmed Alsohaily, Elvino Sousa

The 3rd Generation Partnership Project (3GPP) submitted the 5G New Radio (NR) system specifications to International Telecommunication Union (ITU) as a candidate fifth generation (…

cs.AI2020

A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint

Behzad Khamidehi, Elvino S. Sousa

This paper studies the trajectory optimization problem for an aerial vehicle with the mission of flying between a pair of given initial and final locations. The objective is to min…

eess.SP2019

Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations

Behzad Khamidehi, Elvino S. Sousa

In this paper, the trajectory optimization problem for a multi-aerial base station (ABS) communication network is investigated. The objective is to find the trajectory of the ABSs…

eess.SP2019

Power Efficient Trajectory Optimization for the Cellular-Connected Aerial Vehicles

Behzad Khamidehi, Elvino S. Sousa

Aerial vehicles have recently attracted significant attention in a variety of commercial and civilian applications due to their high mobility, flexible deployment and cost-effectiv…