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

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.

5 pages, 5 figures, accepted by IEEE Communications Letters

References in corpus (5)

• Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization
• Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
• Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
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Cited by in corpus (4)

• Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures
• A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction
• Enhanced ELM Based Channel Estimation for RIS-Assisted OFDM systems with Insufficient CP and Imperfect Hardware
• Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication