Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach
Firas Fredj, Amal Feriani, Amine Mezghani, Ekram Hossain

TL;DR
This paper introduces a variational inference method for separate channel estimation in RIS-enabled mmWave systems, reducing signaling overhead and improving capacity approximation under mobility.
Contribution
It proposes a novel variational inference approach for joint and statistical channel estimation in passive RIS systems, addressing mobility challenges.
Findings
Variational inference achieves near-perfect capacity with estimated CSI.
The method reduces signaling overhead for mobile users.
Joint estimation improves channel accuracy in RIS systems.
Abstract
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…
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.
Taxonomy
TopicsAdvanced Wireless Communication Technologies · Indoor and Outdoor Localization Technologies · Millimeter-Wave Propagation and Modeling
MethodsVariational Inference · Balanced Selection
