Massive multiple-input multiple-output (MIMO) is a promising technology for enabling cellular-connected unmanned aerial vehicle (UAV) communications in the future. Equipped with full-dimensional large arrays, ground base stations (GBSs) can apply adaptive fine-grained three-dimensional (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 the accurate channel state information (CSI) of both UAVs and terrestrial users at the GBSs, which is practically difficult to achieve due to UAV-induced pilot contamination and UAV's 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 about promising directions for future research. Preliminary simulation results are also provided to validate the effectiveness of proposed solutions.
翻译:大型多投入多输出量(MIMO)是今后促进与蜂窝相连的无人驾驶飞行器通信的一个大有希望的技术,地面基地台(GBS)配备全维大型阵列,可以使用适应性细微的三维(3D)波束,以减轻高空无人驾驶飞行器和低空地面用户之间的强烈干扰,从而大大提高网络光谱效率。然而,大型MIO的绩效收益关键取决于全球定位系统无人驾驶飞行器和地面用户的准确频道状态信息,而由于无人驾驶飞行器引发的试点污染和3D的无人驾驶飞行器高度机动性,这实际上很难实现。 此外,日益流行的应用程序依赖大型协调的无人驾驶飞行器或无人驾驶飞行器的三维(3D)以及大型IMO的实用混合GBS波成型结构,使试点污染和频道/波束跟踪问题进一步复杂化。在本篇文章中,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,并讨论了未来研究结果的前景。