基于Grid-PARAFAC毫米波大规模多输入多输出系统联合信道估计算法

针对在毫米波大规模多输入多输出系统中超密集组网存在干扰的问题，提出了基于Grid-PARAFAC（grid-parallel factor analysis）联合信道估计方法。首先，将大规模天线的高维接收信号映射到大尺度张量空间，利用Grid-PARAFAC张量分解将其分解为子张量接收信号，然后对子张量接收信号并行张量分解得到符号、接收天线和子载波的子投影矩阵，最后，通过交替最小二乘准确求得隐藏高维接收信号中的信道信息。通过Grid-PARAFAC张量分解能够在保留原始空间信息的条件下深度挖掘数据隐藏因子，并对其处理而不是整个数据张量，降低了接收信号维度，同时又保留着高维接收信号的空间结构互相关信息；仿真结果表明，所提算法减少了超密集组网所存在高维度信道干扰，降低了计算复杂度，提高了系统性能。

Joint Channel Estimation Algorithm for Millimeter Wave Massive MIMO System Based on GRID-PARAFAC

A grid-parafac joint channel estimation method is proposed to solve the interference problem of super-dense networking in millimeter-wave massive multiple input multiple output systems. Firstly, the high-dimensional received signal of the large-scale antenna is mapped to the large-scale tensor space, which is decomposed into sub-tensor received signals by Grid-PARAFAC tensor decomposition, and then the tensor-received signals are decomposed in parallel to obtain the symbols, receiving antenna and the subcarriers sub-projection matrix. Finally, the channel information in the hidden high-dimensional received signal is obtained by alternate least squares accurately. The Grid-PARAFAC tensor decomposition can mine the data hiding factor under the condition of retaining the original spatial information deeply, and process it instead of the entire data tensor, which reduces the received signal dimension while retaining the spatial structure of the high-dimensional received signal. Simulation shows the proposed algorithm reduces the high-dimensional channel interference and computational complexity in ultra-dense networking and improves the performance of system .