Location-based Robust Beamforming Design for Cellular-enabled UAV Communications

Miao, Wang and Luo, Chunbo and Min, Geyong and Mi, Yang and Yu, Zhengxin (2021) Location-based Robust Beamforming Design for Cellular-enabled UAV Communications. IEEE Internet of Things Journal, 8 (12). pp. 9934-9944. ISSN 2327-4662

[thumbnail of Location-based Robust Beamforming Design for Cellular-enabled UAV Communications]
Text (Location-based Robust Beamforming Design for Cellular-enabled UAV Communications)
Location_based_Robust_Beamforming_Design_for_Cellular_enabled_UAV_Communications.pdf - Submitted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (703kB)

Abstract

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for Unmanned Aerial Vehicles (UAVs), which have been widely deployed to conduct various missions, e.g. precision agriculture, forest monitoring and border patrol. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe inter-cell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of Direction-of-arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14dB SINR gain compared with five benchmark beamforming algorithms, including Linearly Constrained Minimum Variance (LCMV), Position-based beamforming, Diagonal Loading (DL), Robust Capon Beamforming (RCB) and Robust LCMV algorithm.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Internet of Things Journal
Additional Information:
©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1711
Subjects:
?? signal processinginformation systemsinformation systems and managementcomputer science applicationshardware and architecturecomputer networks and communications ??
ID Code:
156592
Deposited By:
Deposited On:
06 Jul 2021 16:15
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Sep 2024 00:55