Hu Rui, Tong Jun, Xi Jiangtao, Guo Qinghua, Yu Yanguang
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.
Sensors (Basel). 2019 Jul 31;19(15):3368. doi: 10.3390/s19153368.
Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as potential candidates for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method.
具有较低硬件复杂度和功耗的混合大规模多输入多输出（MIMO）结构被视为毫米波（mmWave）通信的潜在候选方案。信道协方差信息可用于设计发射机预编码器、接收机合并器、信道估计器等。然而，混合结构仅允许观测低维信号，这增加了信道协方差矩阵估计的难度。在本文中，我们通过克罗内克积展开将信道协方差估计表述为结构化低秩矩阵传感问题，并使用低复杂度算法来解决该问题。提供了均匀线性阵列（ULA）和均匀方形平面阵列（USPA）的数值结果，以证明我们所提方法的有效性。
