Data-Aided MIMO Channel Estimation by Clustering and Reinforcement-Learning

In this paper, we propose a data-aided channel estimator, which can improve the performance of the linear minimum-mean-squared-error (LMMSE) by clustering and reinforcement-learning for multiple-input multiple-output (MI-MO) systems. For clustering-based data detection, we develop a system constrained Gaussian mixture model (SCGMM), in which the a posteriori probabilities (APPs) can be calculated by the expectation-maximization (EM) algorithm. The initial centroids of the SCGMM are sensitive to the channel estimation. To obtain robust channel estimation, we design initial pilots that can reduce the estimated error of the LMMSE. To further improve the quality of channel estimation, we propose a data-aided channel estimation algorithm, which exploits the techniques of coding and reinforcement-learning to obtain soft symbol decisions. Numerical results show that the proposed method can approach the bit-error-rate (BER) performance with perfect channel state information (CSI) in the high signal-to-noise (SNR) region.