CV-3DCNN: Complex-Valued Deep Learning for CSI Prediction in FDD Massive MIMO Systems

Abstract

In beyond fifth-generation (B5G) era, massive multiple-input multiple-output (M-MIMO) will be a key technology to offer higher network capacities. Due to the different frequency of uplink and downlink channels in FDD systems, the channel state information (CSI) feedback from user terminal to the base station is necessary, but this reduces the spectrum efficiency. This letter proposes a deep learning based solution to predict the downlink CSI in frequency division duplex (FDD) systems, which is termed as complex-valued three dimensional convolutional neural network (CV-3DCNN). The proposed network uses a complex-valued neural network in complex domain to deal with the complex CSI matrices, and adopts three-dimensional convolution operations for feature extraction. The proposed scheme aims to make full use of the hidden information of the complex matrices of the CSI data, and to minimize information loss caused by data processing. The experimental results demonstrate that the proposed architecture can improve accuracy of the downlink CSI prediction by approximately 6 dB.