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Guo, J., Wen, C.-K., Jin, S. and Li, G.Y. (2019) Convolutional Neural Network Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis. arXiv preprint arXiv: 1906.06007.

has been cited by the following article:

• TITLE: Multi-Scale and Multi-Channel Networks for CSI Feedback in Massive MIMO System

  AUTHORS: Bingyang Cheng, Jianing Zhao, Yu Hu

  KEYWORDS: Deep Learning, Multi-Scale, Multi-Channel, Massive MIMO

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.10, October 29, 2021

  ABSTRACT: 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.