蜂窝互联移动无人机鲁棒抗干扰波束形成方案

Jiajia Huang, E. Kurniawan, Sumei Sun
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引用次数: 0

摘要

波束形成技术是一种很有前途的蜂窝互联无人机系统抗干扰技术。对于不完全CSI的移动无人机,设计抗干扰波束形成矢量是一个挑战。在本文中,我们考虑了在存在恶意地面干扰器的情况下,从无人机到多天线地面基站的上行传输。我们提出了一种基于深度学习的波束形成网络(DLBF),以最大限度地提高存在地面干扰器的移动无人机的平均数据速率。复杂度分析表明,DLBF具有线性复杂度,在大型天线阵中具有良好的可扩展性。大量仿真结果表明,抗干扰DLBF提高了移动无人机的平均数据速率。DLBF优势在不完全CSI和不同天线配置下都具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Anti-Jamming Beamforming Scheme for Cellular-Connected Mobile UAV
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.
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