摘要：In the frequency division duplex (FDD) mode of the massive MIMO system, the system needs to perform coding through channel state information (CSI) to obtain performance gains. However, the number of antennas of the base station has been greatly increased, resulting in a rapid increase in the overhead for the user terminal to feedback CSI to the base station. In this article, we propose a method based on multi-task CNN to achieve compression and reconstruction of channel state information through a multi-scale and multi-channel convolutional neural network. We also introduce a dynamic learning rate model to improve the accuracy of channel state information reconstruction. The simulation results show that compared with the original CsiNet and other work, the proposed CSI feedback network has better reconstruction performance.
关键词：Deep Learning;Multi-Scale;Multi-Channel;Massive MIMO
