无人机蜂群通信的虚拟大规模MIMO信道估计算法

Virtual Massive MIMO Channel Estimation Algorithm in UAV Swarm Communications

针对利用无人机蜂群通信实现热点区域覆盖的应用场景,提出了一种虚拟大规模多输入多输出(MIMO)信道估计算法,包括信道状态信息中导向矢量的波达方向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,利用辅助用户对无人机方向角进行估计,提出了一种基于降秩的波达方向估计算法,以获取精确的波达方向信息。针对无人机动态位置的变化使不同无人机天线阵列相对位置发生变化的情况,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的高复杂度的问题。仿真结果表明,所提波达方向和子阵间距估计算法可以提高信道的估计精度。

In the application scenario of hot spots coverage with unmanned aerial vehicle(UAV) swarm communications, a channel estimation algorithm for virtual large-scale multiple input multiple output(MIMO) channel in UAV swarm communication is proposed. The proposed channel algorithm includes a direction of arrival(DOA) estimation algorithm and a sub-array spacing estimation algorithm in the steering-vector of the channel state information. Since 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, since 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.