Channel Estimation for Semi-Passive Reconfigurable Intelligent Surfaces With Enhanced Deep Residual Networks
Jin Yu, Jiayi Zhang, Xiaodan Zhang, Huahua Xiao, Bo Ai, Derrick Wing Kwan Ng
Abstract
Reconfigurable intelligent surface (RIS) is envisioned as an essential paradigm for realizing the sixth-generation networks, due to the use of low-cost reflecting elements for establishing programmable and favourable wireless environment. However, accurate channel estimation is a fundamental technical challenge for achieving large performance gains brought by RIS. To address this challenge, we first integrate a RIS with a small number of uniformly distributed active sensing devices, which are equipped with active radio frequency chains for acquiring partial channel state information (CSI). Then, by leveraging the rank-deficient structure of RIS channels, two practical residual neural networks, named single-scale enhanced deep residual (EDSR) and multi-scale enhanced deep residual (MDSR), are proposed to obtain accurate CSI, which can strike a balance between the system complexity and estimation performance. Simulation results reveal the cost-performance trade-off of the two proposed methods and unveil their superior performance compared with existing baseline schemes.