The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (1): 49-56.doi: 10.19682/j.cnki.1005-8885.2024.2005

• Wireless • Previous Articles     Next Articles

Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning

Kang Xiaofei, Wang Tian, Liang Xian   

  1. College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710600, China
  • Received:2023-08-23 Revised:2024-01-01 Accepted:2024-02-22 Online:2024-02-29 Published:2024-02-29
  • Contact: Corresponding author: Kang Xiaofei, E-mail:

Abstract: In response to the challenge posed by the complexity of the system and the difficulty in obtaining accurate channel state information (CSI) for millimeter wave communication assisted by intelligent reflecting surfaces (IRS), we propose a deep learning-based channel estimation scheme. The proposed scheme employs a hybrid active/passive IRS architecture, wherein the least square (LS) algorithm is initially utilized to acquire the channel estimate from the active elements. Subsequently, this estimation is interpolated to obtain a preliminary channel estimation and ultimately refined into an accurate estimate of the channel using the channel super-resolution convolutional neural network (Chan-SRCNN) deep learning network. The simulation results demonstrate that the proposed scheme surpasses LS, orthogonal matching pursuit (OMP), synchronous OMP (SOMP), and deep neural network (DNN) channel estimation algorithms in terms of normalized mean squared error (NMSE) performance, thereby validating the feasibility of the proposed approach.

Key words: intelligent reflecting surface, millimeter wave, channel estimation, deep learning