Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

By Li Wei, Chongwen Huang, George C. Alexandropoulos, Chau Yuen, Zhaoyang Zhang, Merouane Debbah. IEEE Transactions on Communications, 2020

620 citations recorded by the source corpus.

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cf786e15cf9bb07e00d5cb590786671f60904f0d

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Citations in the corpus

A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications (2021, methodology): “To simplify the joint channel estimation in the uplink, the authors in [57], [91] considered the downlink channel training where each user estimates its individual direct/cascaded CSI (i.”
Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques (2022, background): “PARAFAC of cascaded channels are proposed in [164].”
Physics-Based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces (2020, background): “, [8], [13], [22], [23], report significant performance”
Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field? (2021, background): “For future works, one may consider extending the proposed polar-domain representation to the near-ﬁeld channel estimation in reconﬁgurable intelligent surface (RIS) aided communications [30].”
Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design (2021, methodology): “based on the parallel factor framework in [42], [43], which continuously estimates the all channels without too high complexity.”
An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, neutral): “The locations of the BS and MU are q = [0, 0, 40] and p = [90, 30, 0] , respectively, and the locations of three RISs are s1 = [60, 45, 15] , s2 = [50, 50, 5] and s3 = [40, 20, 10] .”