Toggle Main Menu Toggle Search

Open Access padlockePrints

Joint Communication Scheduling and Velocity Control in Multi-UAV-Assisted Sensor Networks: A Deep Reinforcement Learning Approach

Lookup NU author(s): Dr Bo WeiORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institute of Electrical and Electronics Engineers Inc., 2021.

For re-use rights please refer to the publisher's terms and conditions.


© 1967-2012 IEEE.Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect data from ground sensors in remote and hostile areas. A key challenge is the joint design of the velocities and data collection schedules of the UAVs, as inadequate velocities and schedules would lead to failed transmissions and buffer overflows of sensors and, in turn, significant packet losses. In this paper, we optimize jointly the velocity controls and data collection schedules of multiple UAVs to minimize data losses, adapting to the battery levels, queue lengths and channel conditions of the ground sensors, and the trajectories of the UAVs. In the absence of the up-to-date knowledge of the ground sensors' states, a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MADRL-SA) is proposed to allow the UAVs to asymptotically minimize the data loss of the system under the outdated knowledge of the network states at individual UAVs. Numerical results demonstrate that the proposed MADRL-SA reduces the packet loss by up to 54% and 46% in the considered simulation setting, as compared to an existing DRL solution with single-UAV and non-learning greedy heuristic, respectively.

Publication metadata

Author(s): Emami Y, Wei B, Li K, Ni W, Tovar E

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Vehicular Technology

Year: 2021

Volume: 70

Issue: 10

Pages: 10986-10998

Print publication date: 01/10/2021

Online publication date: 08/09/2021

Acceptance date: 03/09/2021

Date deposited: 29/06/2023

ISSN (print): 0018-9545

ISSN (electronic): 1939-9359

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/TVT.2021.3110801


Altmetrics provided by Altmetric