Deep Learning Based Channel Estimation for Intelligent Reflecting Surface Aided MISO-OFDM Systems
Shiyu Liu, Ming Lei, Minjian Zhao
Abstract
Intelligent reflecting surface (IRS) has been proposed as a promising technology to smartly control the wireless signal propagation and enhance the spectral efficiency of wireless communication systems cost-effectively. The channel state information (CSI) is a crucial factor for the design of optimal passive beamforming in the IRS assisted communication systems. However, acquiring such CSI is very challenging for IRS due to its lack of radio frequency (RF) chains. In this paper, we consider an IRS aided multiple-in single-out (MISO) orthogonal frequency-division multiplexing (OFDM) system and propose a deep learning (DL) based channel estimation method to address the above challenges. In particular, a convolutional neural network is designed to estimate both the direct and cascaded channels of the system considered. Simulation results validate that the proposed DL approach achieves better performance than traditional channel estimation techniques.