Two-Stage Channel Estimation for mmWave Massive MIMO Systems Based on ResNet-UNet

For millimeter wave massive multiple-input multiple-output systems, the transceiver usually adopts a hybrid precoding structure to reduce complexity and cost, which poses great challenges to the acquisition of channel state information, especially in the case of low signal-to-noise ratio regime. In this article, residual network (ResNet) is employed to address this problem. Firstly, we design a two-stage channel estimation structure to improve the accuracy of channel estimation. Then, we take ResNet as the basic network and integrate UNet structure to build ResNet-UNet model to solve the model degradation problem. Moreover, we use noise2noise algorithm to train the neural network in order to implement the channel estimation in the case that a clean pilot cannot be obtained. Numerical results show that compared with the traditional channel estimation algorithms and deep convolutional neural network algorithm, the proposed approach has higher accuracy and robustness, and achieves performance close to the denoising algorithm using clean targets that is very difficult to be implemented in practical situations.