In this paper, a channel estimation method based on reduced dimension decomposition for millimeter-wave massive MIMO systems is proposed. For the sake of obtaining the high accuracy of the estimation, we decompose the channel matrix estimation into angle information and channel gain information estimation. The received signals are decomposed by dimensionality reduction so that the angles of the receiving end and transmitting end are separated. The sparse signal recover (SSR) scheme is used to acquire the initial sparse support set. Then, the off-grid error is regarded as the adjustment parameter. Using the orthogonal relationship between the signal subspace and the noise subspace, we gradually approximate the true discrete grid using Taylor’s formula. Finally, the path gain is estimated by the least squares estimation (LSE) algorithm. The benefit of the proposed method is the reduction of the training resources and costs. More importantly, to verify the estimated performance, we obtain the normalized mean square error (NMSE) performance of the channel matrix estimation and the achievable spectral efficiency (ASE) performance under the estimation scheme. Simulation results indicate that the proposed method has the effectiveness and superiority.

提出了一种基于降维分解的毫米波大规模MIMO系统信道估计方法。为了获得高精度的估计，我们将信道矩阵估计分解为角度信息和信道增益信息估计。通过降维来分解接收的信号，使得接收端和发送端的角度分开。稀疏信号恢复（SSR）方案用于获取初始稀疏支持集。然后，将离网误差视为调整参数。利用信号子空间和噪声子空间之间的正交关系，我们使用泰勒公式逐渐逼近真正的离散网格。最后，通过最小二乘估计（LSE）算法估计路径增益。所提出的方法的好处是减少了培训资源和成本。更重要的是，为了验证估计的性能，我们获得了信道矩阵估计的归一化均方误差（NMSE）性能和估计方案下可实现的频谱效率（ASE）性能。仿真结果表明，该方法具有有效性和优越性。