University of Oulu

D. Athukoralage, I. Guvenc, W. Saad and M. Bennis, "Regret Based Learning for UAV Assisted LTE-U/WiFi Public Safety Networks," 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, 2016, pp. 1-7. doi: 10.1109/GLOCOM.2016.7842208

Regret based learning for UAV assisted LTE-U/WiFi public safety networks

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Author: Athukoralage, Dasun1; Guvenc, Ismail1,2; Saad, Walid3;
Organizations: 1Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
2Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA
3Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
4CWC - Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2018080733450
Language: English
Published: Institute of Electrical and Electronics Engineers, 2016
Publish Date: 2018-08-07
Description:

Abstract

Broadband wireless communication is of critical importance during public safety scenarios as it facilitates situational awareness capabilities for first responders and victims. In this paper, the use of LTE-Unlicensed (LTE-U) technology for unmanned aerial base stations (UABSs) is investigated as an effective approach to enhance the achievable broadband throughput during emergency situations by utilizing the unlicensed spectrum. In particular, we develop a game theoretic framework for load balancing between LTE-U UABSs and WiFi access points (APs), based on the users' link qualities as well as the loads at the UABSs and the ground APs. To solve this game, we propose a regret-based learning (RBL) dynamic duty cycle selection (DDCS) method for configuring the transmission gaps in LTE-U UABSs, to ensure a satisfactory throughput for all users. Simulation results show that the proposed RBL-DDCS yields an improvement of 32% over fixed duty cycle LTE-U transmission, and an improvement of 10% over Q-learning based DDCS.

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-L: 2334-0983
ISBN: 978-1-5090-1328-9
ISBN Print: 978-1-5090-1329-6
Pages: 1 - 7
DOI: 10.1109/GLOCOM.2016.7842208
OADOI: https://oadoi.org/10.1109/GLOCOM.2016.7842208
Host publication: 2016 IEEE Global Communications Conference (GLOBECOM)
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is supported in part by NSF under the grant numbers AST-1443999, AST-1506297, ACI-1541105, and CNS-1453678.
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