Channel Estimation for mmWave Massive MIMO With Convolutional Blind Denoising Network

Abstract

Channel estimation is one of the foremost challenges for realizing practical millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To circumvent this problem, deep convolutional neural networks (CNNs) have been recently employed to achieve impressive success. However, current deep CNNs based channel estimators are only suitable to a small range of signal-to-noise ratios (SNRs). Unlike the existing works, the modified convolutional blind denoising network (CBDNet) is proposed to improve the robustness for noisy channel by adopting noise level estimation subnetwork, non-blind denosing subnetwork, and asymmetric joint loss functions for blind channel estimation. Furthermore, the CBDNet can adjust the estimated noise level map to interactively reduce the noise in the channel matrix. Numerical results demonstrate that the proposed CBDNet-based channel estimator can outperform the traditional channel estimators, traditional compressive sensing techniques and deep CNNs in terms of the normalized mean squared error. In addition, the CBDNet can be used over a large range of SNRs, which hugely reduce the cost of offline training.