Channel Parameter Estimation of mmWave MIMO System in Urban Traffic Scene: A Training Channel-Based Method

In frequency selective channel environment, channel estimation in hybrid precoding millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a challenge issue. To solve this problem, we propose an effective channel estimation scheme for frequency selective channel, which is based on the training channel model in urban traffic environment. Considering that the practical mmWave MIMO channel is sparsity and the subcarrier multi-channels have the same sparse structure, we regard the channel estimation problem as the sparse channel recovery, and propose a multipath simultaneous matching tracking estimation method. It is assumed that the noise between the practical channels has a certain correlation, and the noise correlation has an impact on the selection of the optimal atomic support set in the process of channel recovery. Therefore, noise weighting is introduced in our proposed method. The simulation results prove the validity of this proposed method in frequency selective mmWave MIMO channel. Without increasing the complexity of the algorithm, the proposed method can achieve better local performance than the traditional classical methods.