Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2020
Abstract
Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently direct the reflected radio waves towards specific UAVs in order to increase their received signal strengths. Using the recently released 3GPP ground-to-air channel models, we analyze the signal gains at UAVs due to the IRS deployments as a function of UAV height as well as various IRS parameters including size, altitude, and distance from base station. Our analysis suggests that even with a small IRS, we can achieve significant signal gain for UAVs flying above the cellular base station. We also find that the maximum gain can be achieved by optimizing the location of IRS including its altitude and distance to BS.
Keywords
Intelligent Reflective Surface, Unmanned Aerial Vehicle, UAV Communications, Cellular UAV
Discipline
Artificial Intelligence and Robotics | Digital Communications and Networking
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Virtual Conference, May 25-28
First Page
1
Last Page
6
ISBN
9781728131061
Identifier
10.1109/wcnc45663.2020.9120632
Publisher
IEEE
City or Country
Virtual Conference
Citation
MA, Dong; DING, Ming; and HASSAN, Mahbub.
Enhancing cellular communications for UAVs via intelligent reflective surface. (2020). Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Virtual Conference, May 25-28. 1-6.
Available at: https://ink.library.smu.edu.sg/sis_research/7001
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Digital Communications and Networking Commons