Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems

TL;DR

This paper introduces a deep learning-based multi-stage approach to efficiently estimate reconfigurable intelligent surface (RIS) channels in MIMO systems, significantly reducing pilot overhead while maintaining accuracy.

Contribution

It proposes a novel three-stage deep neural network framework that predicts inactive RIS elements' channels, improving estimation efficiency in RIS-assisted MIMO systems.

Findings

Reduces pilot overhead in RIS channel estimation

Maintains high accuracy with fewer pilots

Effective deep learning-based channel prediction

Abstract

This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel. Motivated by the compelling grasp of deep learning in tackling nonlinear mapping problems, the proposed approach only activates a part of RIS elements and utilizes the corresponding cascaded channel estimate to predict another part. Through a synthetic deep neural network (DNN), the direct channel and active cascaded channel are first estimated sequentially, followed by the channel prediction for the inactive RIS elements. A three-stage training strategy is developed for this synthetic DNN. From simulation results, the proposed deep learning based approach is effective in reducing the pilot overhead and guaranteeing the reliable estimation accuracy.