三维信道模型约束下的大规模MIMO信道估计方法

A Channel Estimation Method Constrained by a 3D Channel Model for Massive MIMO

针对配备1 bit模数转换器(ADC)的大规模多输入多输出(MIMO)上行信道估计精度差的问题,提出了一种三维信道模型约束下的大规模MIMO信道估计方法。首先,结合所选定的适用于面天线阵的三维空间信道模型,建立了带有信道模型约束的大规模MIMO信道估计优化问题,并在优化目标函数的接收信号误差项中引入随机白噪声扰动,以提升方法的抗噪能力;其次,采用交替迭代式算法对优化问题进行求解,推导了信道模型参数的更新过程;最后,通过采用信道模型空间结构并使用噪声功率来恢复低精度接收信号的振幅,进而实现更准确的信道参数提取。仿真结果表明:与最小二乘估计和基于Bussgang分解的线性最小均方误差估计相比,所提方法可有效降低量化噪声和白噪声的影响,提高信道估计精度;当信噪比为10 dB时,采用所提方法估计信道的均方误差比基于Bussgang分解的线性最小均方误差估计方法减小了75%。

A channel estimation scheme constrained by a 3-dimensional(3D)channel model for the massive multi-input and multi-output(MIMO)systems is proposed to improve the estimation accuracy of large scale MIMO channels equipped with one-bit quantized analog-to-digital converters(ADCs).Firstly,a channel estimation optimization problem with a spatial channel model constraint is established for massive MIMO by combining a selected 3D space channel model suitable for the planar array antennas at the base station.Moreover,in order to improve the anti-noise ability of the method,a random white noise term is introduced into the error term of the received signal of the optimization objective function.Secondly,an alternating iterative algorithm is adopted to solve the optimization problem,and an updating process for the channel model parameters is derived in detail.Finally,the amplitude of the low-precision received signal is recovered by using the structure of the channel model and the power of noise,so as to achieve more accurate channel parameter extraction.Simulation results and comparisons with the least squares(LS)estimation and the linear minimum mean square error estimation(LMMSE)based on Bussgang decomposition show that the proposed scheme effectively reduces the influence of the quantization noise and the white noise and improves the accuracy of channel estimation.When the signal to noise ratio(SNR)is 10 dB,the proposed method reduces the mean square error estimation of the channel by 75%compared with the linear minimum mean square error estimation algorithm based on Bussgang decomposition.