Deep Learning Compressed Sensing-Based Beamspace Channel Estimation in mmWave Massive MIMO Systems
Weiqiang Tong, Wenjun Xu, Fengyu Wang, Jin Shang, Miao Pan, Jiaru Lin
Abstract
Channel estimation attaches great importance in millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems. This letter proposes a two-step orthogonal matching pursuit (OMP) method to estimation channel state information (CSI) based on deep learning compressed sensing. Specifically, in the first-step OMP, a composite convolution kernel function (CKF) is designed for coarsely estimating angles of arrival/departure (AoAs/AoDs) from correlation matrix. In the second-step OMP, a Squeeze-and-Excitation Residual network (SE-Resnet) with Noise2Void (N2V) learning strategy is presented to denoise correlation matrix and finely estimate AoAs/AoDs. The proposed method can work without labeled data. Simulation shows that the two-step OMP significantly outperforms state-of-the-art mmWave channel estimation methods. Moreover, it works robustly in low signal-to-noise ratio (SNR) regimes with a small number of training frames.