Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO
Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang

TL;DR
This paper introduces a matrix-calibration-based method for efficient channel estimation in RIS-assisted multiuser MIMO systems, reducing training overhead while maintaining high accuracy through a novel message-passing algorithm.
Contribution
It proposes a new matrix calibration approach for cascaded channel estimation that leverages channel sparsity and slow-varying components, with an analytical performance analysis.
Findings
High accuracy in channel estimation demonstrated in simulations
Reduced training overhead compared to state-of-the-art methods
Theoretical performance bounds established for the estimator
Abstract
Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in an RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
