Location-Based Robust Beamforming Design for Cellular-Enabled UAV Communications

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, 3-D mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe intercell 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 the 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 14-dB 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.