Abstract
Reconfigurable intelligent surface (RIS) composed of a large number of passive components can significantly save hardware cost and energy. However, its channel estimation is challenging since RIS commonly operates as a passive reflecting device and cannot acquire the channel state information (CSI) independently. In this paper, a data-driven approach for achieving highly accurate channel estimation in RIS assisted multi-user system is proposed. First, the channel estimation problem is modeled as a residual noise cancellation problem, and then the channel matrix with noise is obtained by a scaled least square (SLS) channel estimation. Finally, an enhanced deep residual shrinkage network (EDRSN) is designed to reduce the noise and further improve the accuracy of channel estimation.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
