Intelligent Reflecting Surface Assisted MISO Downlink: Channel Estimation and Asymptotic Analysis

Bayan Al-Nahhas, Qurrat Ul Ain Nadeem, Anas Chaaban

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This work makes the preliminary contribution of studying the asymptotic performance of a multi-user intelligent reflecting surface (IRS) assisted-multiple-input single-output (MISO) downlink system under imperfect CSI. We first extend the existing least squares (LS) ON/OFF channel estimation protocol to a multi-user system, where we derive minimum mean squared error (MMSE) estimates of all IRS-assisted channels over multiple sub-phases. We also consider a low-complexity direct estimation (DE) scheme, where the BS obtains the MMSE estimate of the overall channel in a single sub-phase. Under both protocols, the BS implements maximum ratio transmission (MRT) precoding while the IRS design is studied in the large system limit, where we derive deterministic equivalents of the signal-to-interference-plus-noise ratio (SINR) and the sum-rate. The derived asymptotic expressions, which depend only on channel statistics, reveal that under Rayleigh fading IRS-to-users channels, the IRS phase-shift values do not play a significant role in improving the sum-rate but the IRS still provides an array gain. Simulation results confirm the accuracy of the derived deterministic equivalents and show that under Rayleigh fading, the IRS gains are more significant in noise-limited scenarios. We also conclude that the DE of the overall channel yields better performance when considering large systems.
Original languageEnglish (US)
Title of host publicationGLOBECOM 2020 - 2020 IEEE Global Communications Conference
PublisherIEEE
ISBN (Print)9781728182988
DOIs
StatePublished - Jan 25 2021
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-07-01
Acknowledged KAUST grant number(s): OSR-2018-CRG7-3734
Acknowledgements: This work is supported by the King Abdullah University of Science and Technology (KAUST) under Award No. OSR-2018-CRG7-3734.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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