Deep Neural Network-Based Channel Estimation Scheme in Massive MIMO Systems

Channel estimation is one of the most essential technologies of wireless communication, as well as necessary indicators to measure the system’s quality. In recent years, the combination of channel estimation and deep learning techniques has raised wide attention and become one of the most popular research directions of machine learning applications in the wireless communication area. However, in wireless communication systems such as massive MIMO, it’s too complicated to take traditional channel estimation methods with shortcomings like insufficient precision or increased cost. With the combination of deep neural networks (DDN) and the least squares (LS) method, this paper focuses on the deep learning-based channel estimation for massive MIMO systems. Compared with traditional algorithms such as LS or minimum mean-square error method, the loss function of the results is significantly reduced while simulating for the same analog channel. The problem of lack of precision has been improved efficiently and the performance has been improved substantially, which can prove the adequacy of the design of the channel estimator.