Deep Learning Based Channel Estimation for MIMO Systems with Received SNR Feedback

Channel estimation with received signal-to-noise ratio (SNR) feedback is promising and effective for practical wireless multiple-input multiple-output (MIMO) systems. In this paper, we investigate the channel estimation problem for the MIMO system with received SNR feedback, of which goal is to estimate the MIMO channel coefficients at a transmitter based on the received SNR feedback information from a receiver in the sense of minimizing the mean square error (MSE) of the channel estimation. For analysis, we consider two very common and widely adopted scenarios of fading: (i) quasi-static block fading and (ii) time-varying fading. In both fading scenarios, it is generally challenging to analytically tackle the channel estimation problem due to its nonlinearity and nonconvexity. To intelligently and effectively address this issue, deep learning is exploited in this paper. First, in the quasi-static block fading scenario, we propose a novel learning scheme for joint channel estimation and pilot signal design by constructing a deep autoencoder via a convolutional neural network (CNN). Also, in the time-varying fading scenario, a novel channel estimation scheme is developed by connecting a recurrent neural network (RNN) to a CNN. Moreover, in both fading scenarios, we present new and effective ways to train the proposed schemes using generative adversarial networks (GANs) to address the practical issue of a limited number of actual channel samples (i.e., real-world data) required for training. Through extensive numerical simulations, we demonstrate effectiveness and superior performance of the proposed schemes.