Deep MIMO Detection with Imperfect CSI

Document Type : Original Article


Faculty of Electrical and Computer Engineering, Malek Ashtar University of Technology, Tehran, Iran


It is possible to noticeably increase the capacity of wireless communication systems through the use of multiple antennas both in the transmitter and in the receiver. In such systems, which are referred to in short as MIMO, the receiver uses its knowledge of the channel to detect the transmitted signal. Different methods have been proposed for optimal and sub-optimal detection of the transmitted signals. Recently, principles of deep learning and implementing neural networks have been employed as a near optimal approach for MIMO detection with fewer calculations during the testing process compared to traditional methods. In the event an error occurs in the receiver’s channel estimation process, this type of detector suffers a drop in performance and as a result, BER will increase. Given that in practice, the receiver only has an estimation of the CSI instead of the exact values, the current study presents an enhanced detection method based on deep learning, which is also robust against channel estimation error. In this detection method, by using the covariance matrix of the channel estimator and the principles of deep learning, a robust detector against channel estimation error is proposed and comprehensively evaluated. Numerical simulations confirm the performance of the proposed method.


Main Subjects

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