# Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach

arXiv: https://arxiv.org/abs/2308.13616 · HTML: https://mlsanity.com/paper/2308.13616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 63 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Implicit Reparameterization Gradients](https://mlsanity.com/paper/1805.08498/) (2018)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications](https://mlsanity.com/paper/2110.01292/) (2021)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications](https://mlsanity.com/paper/2009.01423/) (2020)
- [Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO](https://mlsanity.com/paper/1905.07948/) (2019)
- [Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2006.02201/) (2020)
- [Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach](https://mlsanity.com/paper/2008.04766/) (2020)
- [Reconfigurable Intelligent Surfaces vs. Relaying: Differences, Similarities, and Performance Comparison](https://mlsanity.com/paper/1908.08747/) (2019)
- [Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication](https://mlsanity.com/paper/2009.01607/) (2020)
- [Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems](https://mlsanity.com/paper/2106.14792/) (2021)
- [Target Sensing with Intelligent Reflecting Surface: Architecture and Performance](https://mlsanity.com/paper/2201.09091/) (2022)
- [Channel Vector Subspace Estimation from Low-Dimensional Projections](https://mlsanity.com/paper/1509.07469/) (2015)
- [Intelligent Reflecting Surface Assisted Wireless Communication: Modeling and Channel Estimation](https://mlsanity.com/paper/1906.02360/) (2019)
- [Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning](https://mlsanity.com/paper/2006.15138/) (2020)
- [TRICE: A Channel Estimation Framework for RIS-Aided Millimeter-Wave MIMO Systems](https://mlsanity.com/paper/2008.09499/) (2020)
- [A General Framework for RIS-Aided mmWave Communication Networks: Channel Estimation and Mobile User Tracking](https://mlsanity.com/paper/2009.01180/) (2020)
- [Intelligent Reflecting Surface-Assisted Wireless Key Generation for Low-Entropy Environments](https://mlsanity.com/paper/2010.06613/) (2020)
- [Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Approaches](https://mlsanity.com/paper/2107.11605/) (2021)
- [Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements](https://mlsanity.com/paper/2206.06605/) (2022)
- [Reconfiguring Wireless Environment via Intelligent Surfaces for 6G: Reflection, Modulation, and Security](https://mlsanity.com/paper/2208.10931/) (2022)
- [A Comparison between Network-Controlled Repeaters and Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2211.06974/) (2022)
- [RIS-Assisted Quasi-Static Broad Coverage for Wideband mmWave Massive MIMO Systems](https://mlsanity.com/paper/2203.00400/) (2022)

## Abstract (arXiv)

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.