Massive multiple-input multiple-output (MIMO) is a promising technology for enabling cellu-lar-connected unmanned aerial vehicle (UAV) communications in the future. Equipped with full-dimensional large arrays, ground base stations (GBSs) can apply adaptive fine-grained 3D beamforming to mitigate the strong interference between high-altitude UAVs and low-altitude terrestrial users, thus significantly enhancing the network spectral efficiency. However, the performance gain of massive MIMO critically depends on accurate channel state information of both UAVs and terrestrial users at the GBSs, which is practically difficult to achieve due to UAV-in-duced pilot contamination and UAVs' high mobility in 3D. Moreover, the increasingly popular applications relying on a large group of coordinated UAVs or UAV swarm as well as the practical hybrid GBS beamforming architecture for massive MIMO further complicate the pilot contamination and channel/beam tracking problems. In this article, we provide an overview of the above challenging issues, propose new solutions to cope with them, and discuss promising directions for future research. Preliminary simulation results are also provided to validate the effectiveness of the proposed solutions.

中文翻译：

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蜂窝连接无人机的大规模MIMO：挑战和有前途的解决方案

大规模多输入多输出（MIMO）是一项有前途的技术，可在未来实现蜂窝连接的无人机（UAV）通信。配备了全尺寸大型阵列的地面基站（GBS）可以应用自适应细粒度3D波束成形，以减轻高空无人机和低空地面用户之间的强烈干扰，从而显着提高网络频谱效率。但是，大规模MIMO的性能增益主要取决于UAV和地面用户在GBS上的准确通道状态信息，由于UAV引起的飞行员污染和UAV在3D中的高度移动性，实际上很难实现。而且，依赖于大量协作式UAV或UAV群的日益流行的应用程序以及用于大规模MIMO的实用混合式GBS波束成形架构进一步使导频污染和信道/波束跟踪问题复杂化。在本文中，我们概述了上述具有挑战性的问题，提出了应对这些问题的新解决方案，并讨论了未来研究的有希望的方向。还提供了初步的仿真结果，以验证所提出解决方案的有效性。