CSI Feedback Based on Complex Neural Network for Massive MIMO Systems

Abstract

In order to solve the problem of large channel state information (CSI) feedback overhead and low feedback accuracy in massive multiple-input multiple-output (MIMO) systems. We propose a CSI feedback method based on complex-valued convolutional neural networks to improve the representation capability of the network. In this method, a complex-valued encoder-decoder structure is constructed considering the fact that CSI exists in the form of complex numbers. We use complex convolutional downsampling (CCD) to extract CSI features in the encoder, and reconstruct the compressed CSI with high accuracy in the decoder by using a complex dense block (CDBlock). Simulation results show that the average accuracy is improved by 17.5% compared with several classical deep learning CSI feedback methods. Our proposed CSI feedback method has higher feedback accuracy and better system performance in massive MIMO systems.