Deep-Learning-Based Robust Channel Estimation for MIMO IoT Systems

When the second-order statistics of channel and noise, such as their covariance matrices, are not exactly known, the acquisition of accurate channel state information (CSI) for a wireless propagation environment becomes quite challenging. In this article, we tackle the problem of robust channel estimation for multiple-input-multiple-output (MIMO)-aided Internet of Things (IoT) systems in the presence of uncertainties in the channel and noise covariance matrices. Our goal is to minimize the mean square error (MSE) of the channel estimation under the channel and noise covariance uncertainties by jointly optimizing the channel estimator and pilot signal, which is however highly nonconvex and mathematically intractable. To effectively and intelligently cope with this issue, we exploit a deep learning (DL) technique and propose a novel network architecture with two modules, namely, the pilot optimizer and channel predictor, both of which are designed by neural networks with their own local connections and weight sharings. Moreover, a novel and effective training strategy for the proposed DL model is devised in a self-supervised manner, in which samples obtained by properly compensated channel and noise covariance matrices are utilized to overcome any adverse impacts of the underlying uncertainties on the channel estimation. Through extensive numerical results simulated in realistic propagation environments, we substantiate the superior performance and effectiveness of the proposed scheme.