Robust Anti-Jamming Beamforming Scheme for Cellular-Connected Mobile UAV

Abstract

Beamforming is a promising anti-jamming technique for cellular-connected unmanned aerial vehicle (UAV) system. It is challenging to design anti-jamming beamforming vectors for moving UAV with imperfect CSI. In this paper, we consider an uplink transmission from UAV to multi-antenna ground base station (BS) in the presence of a malicious ground jammer. We propose a deep learning based beamforming network (DLBF) to maximize the average data rate for moving UAV in the presence of a ground jammer. Complexity analysis shows that DLBF has linear complexity, which indicates good scalability in large antenna arrays. Extensive simulation results show that anti-jamming DLBF improves average data rate for moving UAV. The performance of DLBF advantage is robust under imperfect CSI and different antenna configurations.