@inproceedings{368fa617385a414da8025d994d16662d,
title = "An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator",
abstract = "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. We show that the estimation performance are dependent on the setting of the IRS, and 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 the discrete Fourier transforms. We show theoretically and with simulations that the estimation variance is one order smaller compared to existing methods with on/off IRS activation patterns as proposed in the literature.",
keywords = "Channel Estimation, Intelligent Reflecting Surfaces, Least Squares, Minimum Variance Unbiased Estimator",
author = "Tobias Jensen and {De Carvalho}, Elisabeth",
year = "2020",
month = apr,
day = "9",
doi = "10.1109/ICASSP40776.2020.9053695",
language = "English",
isbn = "978-1-5090-6632-2",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "IEEE",
pages = "5000--5004",
booktitle = "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
address = "United States",
note = "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 04-05-2020 Through 08-05-2020",
}