无人机蜂群通信的虚拟大规模MIMO信道估计算法

针对利用无人机蜂群通信实现热点区域覆盖的应用场景，提出了一种无人机蜂群通信中虚拟大规模多输入多输出信道估计算法，包括信道状态信息中导向矢量的波达方向(DOA, Direction Of Arrival)估计算法和子阵间距估计算法。考虑到空地信道状态依赖于地面用户的角域信息，因此先利用辅助用户对无人机方向角进行估计，在此基础上提出了一种基于降秩的DOA估计算法，获取精确的DOA信息。进一步，考虑到UAV动态位置变化导致不同UAV的天线阵列相对位置变化，提出了一种基于优化搜索的子阵间距估计算法，避免了大范围搜索带来计算复杂度高的问题。仿真结果表明，所提DOA和子阵间距估计算法可以提高信道估计精度。

Virtual Massive MIMO Channel Estimation Algorithm in UAV Swarm Communications

In the application scenario of hot spots coverage with UAV (unmanned aerial vehicle) swarm communications, a channel estimation algorithm for virtual large-scale multiple input multiple output channel in UAV swarm communication is proposed. The proposed channel algorithm includes a direction of arrival (DOA) estimation algorithm and sub-array spacing estimation algorithm in the steering-vector of the channel state information. Considering that the air to ground channel state depends on the angle domain information of the ground users, the auxiliary user is used to estimate the direction angle of the UAV. Based on this, a reduced rank based DOA estimation algorithm is proposed to obtain high-precision DOA information. Furthermore, considering that the dynamic position change of UAV results in the relative position change of antenna arrays of different UAVs, a sub-array spacing estimation algorithm based on optimization search is proposed to avoid the high computational complexity caused by large-scale search. Simulation results show that the proposed DOA and sub-array spacing estimation algorithm can improve the accuracy of channel estimation.