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2023•Unpublished venueRequires access

Channel Estimation for RIS aided MISO System

Rıfat Volkan Şenyuva

Open publisher page 2 citations

Abstract

This paper considers the channel estimation of a single user in a MISO system with an intelligent reflecting surface (IRS). The performances of the minimum variance unbiased (MVU) and minimum mean square error (MMSE) estimators using the discrete Fourier transform activation pattern for the IRS, updated at every symbol interval, are compared. Numerical results show that the MMSE estimator provides over 10 dB SNR improvement compared to the MVU estimator.

About this research paper

What this paper is about

This paper considers the channel estimation of a single user in a MISO system with an intelligent reflecting surface (IRS). The performances of the minimum variance unbiased (MVU) and minimum mean square error (MMSE) estimators using the discrete Fourier transform activation pattern for the IRS, updated at every symbol interval, are compared. Numerical results show that the MMSE estimator provides over 10 dB SNR improvement compared to the MVU estimator.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper considers the channel estimation of a single user in a MISO system with an intelligent reflecting surface (IRS). The performances of the minimum variance unbiased (MVU) and minimum mean square error (MMSE) estimators using the discrete Fourier transform activation pattern for the IRS, updated at every symbol interval, are compared. Numerical results show that the MMSE estimator provides over 10 dB SNR improvement compared to the MVU estimator.

Key concepts: Estimator, Minimum mean square error, Mean squared error, Minimum-variance unbiased estimator, Channel (broadcasting), Computer science, Algorithm, Discrete Fourier transform (general)

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