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Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach
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DOI：10.1109/TWC.2024.3371703.png)
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Abstract
En 中文
Channel estimation in reconfigurable intelligent surfaces (RIS)-aided systems is crucial for optimal configuration of the RIS and various downstream tasks such as user localization. In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links. In the literature, two approaches are proposed: (i) cascaded channel estimation where the two channels are collapsed into a single one and estimated using training signals at the BS, and (ii) separate channel estimation that estimates each channel separately either in a passive or semi-passive RIS setting. In this work, we study the separate channel estimation problem in a fully passive RIS-aided millimeter-wave (mmWave) single-user single-input multiple-output (SIMO) communication system. First, we adopt a variational-inference (VI) approach to jointly estimate the UE-RIS and RIS-BS instantaneous channel state information (I-CSI). In particular, auxiliary posterior distributions of the I-CSI are learned through the maximization of the evidence lower bound. However, estimating the I-CSI for both links in every coherence block results in a high signaling overhead to control the RIS in scenarios with highly mobile users. Thus, we extend our first approach to estimate the slow-varying statistical CSI of the UE-RIS link overcoming the highly variant I-CSI. Precisely, our second method estimates the I-CSI of RIS-BS channel and the UE-RIS channel covariance matrix (CCM) directly from the uplink training signals in a fully passive RIS-aided system. The simulation results demonstrate that using maximum a posteriori channel estimation using the auxiliary posteriors can provide a capacity that approaches the capacity with perfect CSI. Leveraging the UE-RIS CCM enhances spectral efficiency by minimizing the training overhead required to control the RIS, and exploiting its low-rank structure reduces training overhead compared to the maximum likelihood estimator.
Keywords:
Channel estimation
Training
Millimeter wave communication
Estimation
Reconfigurable intelligent surfaces
Wireless communication
Covariance matrices
Reconfigurable intelligent surface (RIS)
channel estimation
statistical channel state information
variational inference (VI)
mmWave communications
spatial channel covariance estimation
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W