Channel Estimation in IRS-Enhanced mmWave System With Super-Resolution Network
Yazheng Wang, Hancheng Lu, Huan Sun
Abstract
To solve the blockage effect in millimeter wave (mmWave) communication, intelligent reflecting surface (IRS) is introduced to create additional links and enhance the system performance, by properly optimizing the IRS phase shifts based on the channel state information (CSI). However, channel estimation in IRS-enhanced mmWave system is challenging, since IRS is unable to perform signal processing and the large number of reflecting elements of IRS leads to high complexity. To reduce the overhead and obtain accurate CSI, we propose a channel estimation scheme based on least square (LS) estimation with partial on-off and super-resolution (SR) network. Specifically, we switch on part of the reflecting elements and estimate the cascaded channel matrix, which can be considered as a low-resolution (LR) image with low-precision. Then it is expanded to a high-resolution (HR) image with low-precision by linear interpolation. Furthermore, we feed this HR image into an SR network to improve the estimation accuracy. Numerical results demonstrate the advantages of our proposed SR channel estimation compared with benchmark schemes.