University of Oulu

A. Shamsoshoara, F. Afghah, E. Blasch, J. Ashdown and M. Bennis, "UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning," in IEEE Open Journal of the Communications Society, vol. 2, pp. 738-753, 2021, doi: 10.1109/OJCOMS.2021.3067001

UAV-assisted communication in remote disaster areas using imitation learning

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Author: Shamsoshoara, Alireza1; Afghah, Fatemeh1; Blasch, Erik2;
Organizations: 1School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
2Information Directorate, Air Force Research Laboratory, Rome, NY 13441, USA
3Centre for Wireless Communications, University of Oulu, 90570 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021111755730
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-11-17
Description:

Abstract

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users’ information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

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Series: IEEE open journal of the Communications Society
ISSN: 2644-125X
ISSN-E: 2644-125X
ISSN-L: 2644-125X
Volume: 2
Pages: 738 - 753
DOI: 10.1109/OJCOMS.2021.3067001
OADOI: https://oadoi.org/10.1109/OJCOMS.2021.3067001
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-20-1-0090 and the National Science Foundation under Grant Numbers CNS-2034218, CNS-2039026, and ECCS-2030047. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. government or AFRL. Distribution A: Approved for Public Release, distribution unlimited. Case Number AFRL-2021-1039 on March 30, 2021.
Copyright information: © 2021 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/