Channel Estimation for mmWave Massive MIMO Systems based on Deep Learning

Abstract

Channel estimation is a key part of communication systems. However, the channel of millimeter-Wave (mmWave) Massive Multiple-Input Multiple-Output (Massive-MIMO) system has sparse characteristics, and the conventional channel estimation method is prone to noise factors and tends to achieve low estimation accuracy. Therefore, in this paper a channel estimation method is proposed for mmWave Massive MIMO systems based on deep learning. Firstly, a dataset to simulate a real-world environment, is generated by setting specific parameters. Furthermore, the generated channel matrix is adopted as the input of the neural network. Secondly, the attention mechanism is integrated into the deep learning method with ResUNet to enhance the ability of feature extraction. Finally, the channel estimation matrix is obtained via the aforementioned network model. The experimental results indicate that the Massive-MIMO method is superior to the conventional channel estimation methods in channel estimation accuracy and convergence rate, and has a good application prospects.