# Learning to Estimate RIS-Aided mmWave Channels

arXiv: https://arxiv.org/abs/2107.12631 · HTML: https://mlsanity.com/paper/2107.12631/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation](https://mlsanity.com/paper/1802.08397/) (2018)
- [Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://mlsanity.com/paper/2001.11085/) (2020)
- [Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1906.03165/) (2019)
- [Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO](https://mlsanity.com/paper/1905.07948/) (2019)
- [Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks](https://mlsanity.com/paper/2010.12004/) (2020)
- [Radio Localization and Mapping with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1912.09401/) (2019)
- [Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization](https://mlsanity.com/paper/2007.08158/) (2020)
- [Channel Estimation for RIS-Aided mmWave MIMO Channels](https://mlsanity.com/paper/2002.06453/) (2020)
- [Using any Surface to Realize a New Paradigm for Wireless Communications](https://mlsanity.com/paper/1806.04585/) (2018)

## Built on by (most cited first, 4 of 4)

- [BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2503.10432/) (2025)
- [Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet](https://mlsanity.com/paper/2109.07729/) (2021)
- [Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices](https://mlsanity.com/paper/2507.09627/) (2025)
- [Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design](https://mlsanity.com/paper/2507.15367/) (2025)

## Abstract (arXiv)

Inspired by the remarkable learning and prediction performance of deep neural networks (DNNs), we apply one special type of DNN framework, known as model-driven deep unfolding neural network, to reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) single-input multiple-output (SIMO) systems. We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.