# An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator

arXiv: https://arxiv.org/abs/1909.09440 · HTML: https://mlsanity.com/paper/1909.09440/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning](https://mlsanity.com/paper/2111.04971/) (2021)
- [Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition](https://mlsanity.com/paper/2501.08007/) (2025)
- [Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments](https://mlsanity.com/paper/2107.07423/) (2021)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)
- [Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging](https://mlsanity.com/paper/2406.07387/) (2024)
- [Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO](https://mlsanity.com/paper/2605.15032/) (2026)

## Abstract (arXiv)

In a wireless system with Intelligent Reflective Surfaces (IRS) containing many passive elements, we consider the problem of channel estimation. All the links from the transmitter to the receiver via each IRS elements (or groups) are estimated. As the estimation performance are dependent on the setting of the IRS, we design an optimal channel estimation scheme where the IRS elements follow an optimal series of activation patterns. The optimal design is guided by results for the minimum variance unbiased estimation. The IRS setting during the channel estimation period mimics a series of discrete Fourier transforms. We show theoretically and with simulations that the estimation variance is one order smaller compared to existing on/off methods proposed in the literature.