research-article

A deep learning approach to efficient drone mobility support

Authors:

Mohammad MozaffariAuthors Info & Claims

DroneCom '20: Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

Pages 67 - 72

https://doi.org/10.1145/3414045.3415948

Published: 07 October 2020 Publication History

Abstract

The growing deployment of drones in a myriad of applications relies on seamless and reliable wireless connectivity for safe control and operation of drones. Cellular technology is a key enabler for providing essential wireless services to drones flying in the sky. Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users, but there are challenges such as mobility support. In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network. Using tools from deep reinforcement learning, we develop a deep Q-learning algorithm to dynamically optimize handover decisions to ensure robust connectivity for drone users. Simulation results show that the proposed framework significantly reduces the number of handovers at the expense of a small loss in signal strength relative to the baseline case where a drone always connect to a base station that provides the strongest received signal strength.

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Cited By

Sun CFontanesi GCanberk BMohajerzadeh AChatzinotas SGrace DAhmadi H(2024)Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning TechniquesIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.34010245(825-854)Online publication date: 2024
https://doi.org/10.1109/OJVT.2024.3401024
Assenine MBechkit WMokhtari IRivano HBenatchba K(2024)Cooperative Deep Reinforcement Learning for Dynamic Pollution Plume Monitoring Using a Drone FleetIEEE Internet of Things Journal10.1109/JIOT.2023.332824211:5(7325-7338)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3328242
Zaid MKadir MShayea IMansor Z(2024)Machine learning-based approaches for handover decision of cellular-connected drones in future networks: A comprehensive reviewEngineering Science and Technology, an International Journal10.1016/j.jestch.2024.10173255(101732)Online publication date: Jul-2024
https://doi.org/10.1016/j.jestch.2024.101732
Show More Cited By

Index Terms

A deep learning approach to efficient drone mobility support
1. Networks
  1. Network properties
    1. Network mobility
  2. Network types
    1. Mobile networks

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cover image ACM Conferences

DroneCom '20: Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

September 2020

105 pages

ISBN:9781450381055

DOI:10.1145/3414045

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 07 October 2020

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MobiCom '20

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SIGMOBILE

MobiCom '20: The 26th Annual International Conference on Mobile Computing and Networking

September 25, 2020

London, United Kingdom

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Cited By

Sun CFontanesi GCanberk BMohajerzadeh AChatzinotas SGrace DAhmadi H(2024)Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning TechniquesIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.34010245(825-854)Online publication date: 2024
https://doi.org/10.1109/OJVT.2024.3401024
Assenine MBechkit WMokhtari IRivano HBenatchba K(2024)Cooperative Deep Reinforcement Learning for Dynamic Pollution Plume Monitoring Using a Drone FleetIEEE Internet of Things Journal10.1109/JIOT.2023.332824211:5(7325-7338)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3328242
Zaid MKadir MShayea IMansor Z(2024)Machine learning-based approaches for handover decision of cellular-connected drones in future networks: A comprehensive reviewEngineering Science and Technology, an International Journal10.1016/j.jestch.2024.10173255(101732)Online publication date: Jul-2024
https://doi.org/10.1016/j.jestch.2024.101732
Deng YMeer IZhang SOzger MCavdar C(2023)D3QN-Based Trajectory and Handover Management for UAVs Co-Existing with Terrestrial Users2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)10.23919/WiOpt58741.2023.10349832(103-110)Online publication date: 24-Aug-2023
https://doi.org/10.23919/WiOpt58741.2023.10349832
Alshaibani WShayea ICaglar RDin JDaradkeh Y(2022)Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous NetworksSensors10.3390/s2216601322:16(6013)Online publication date: 12-Aug-2022
https://doi.org/10.3390/s22166013
Amer RBaza MGalkin BAlourani AMarchetti N(2022)On the Performance of Mobile Cellular-Connected Drones Under Practical Antenna ConfigurationsIEEE Transactions on Vehicular Technology10.1109/TVT.2022.316945071:7(7548-7560)Online publication date: Jul-2022
https://doi.org/10.1109/TVT.2022.3169450

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