Deep Learning Based CSI Compression and Quantization With High Compression Ratios in FDD Massive MIMO Systems

Abstract

Generally, the downlink channel state information (CSI) used for precoding is conveyed back in massive multi-input multi-output (MIMO) systems with frequency division duplex (FDD) mode, which costs too much bandwidth. However, most of CSI compression algorithms including deep learning based schemes show poor performance with high compression ratio (CR) in ourdoor scenery. In this letter, we propose a new neural network that utilizes the deep neural network (DNN) to improve the learning ability of residual network and a novel activation function to adapt the neural network. Simulation results show that the proposed CsiNet+DNN method exhibits better performance than exsiting methods in recovery quality and accuracy, which also achieves remarkable robustness at noise situations.