随着无人机技术的快速发展，在无人机通信平台上配置大规模MIMO技术已成为业界的研究热点。大规模MIMO技术通过挖掘空间维度无线资源，可以明显的提升系统的频谱效率和功率效率。但是，将大规模MIMO技术直接应用于无人机通信系统，会导致算法复杂度高和硬件开销大等问题。本项目拟开展基于统计信道信息的无人机大规模MIMO信道建模和双层预编码算法设计。首先依据信道的传播特性，建立无人机MIMO信道模型，得到空时相关性函数，利用随机矩阵和凸优化理论等数学工具分析无人机MIMO系统的信道容量；其次，建立无人机大规模MIMO和毫米波大规模MIMO信道模型，分析影响相关性函数变化的关键因素；最后，利用本项目大规模MIMO信道模型的空时相关性函数，进行低复杂度的双层预编码算法的设计，为提出高速率、低复杂度的5G无人机通信系统传输方案提供理论支撑。

With the rapid development of unmanned aerial vehicle (UAV) technology, it has become a research hotspot to configure massive MIMO technology on UAV communication platform. Massive MIMO technology can significantly improve the spectrum and power efficiency of the system by utilizing spatial dimension wireless resources. However, the massive MIMO technique also brings the drawback such as high implementation complexity and high hardware when directly applied to UAV systems. In this project, we plan to investigate the channel modeling and two-stage precoding algorithm design for UAV massive communication systems. Firstly, we formulate the correlation model and derive space-time correlation function based on the propagation characteristics in UAV. In addition, we use various mathematical tools such as the stochastic matrix and the convex optimization theory to analyze the channel capacity in UAV-MIMO systems. Secondly, the UAV massive MIMO and mmWave massive MIMO channel models are established, and the key factors affecting the change of correlation function are analyzed. Finally, the two-stage precoding algorithm with low complexity is designed by using the space-time correlation function of the 5G model of the project, which provides theoretical support for the transmission scheme of 5G UAV communication systems with high speed and low complexity.