Cascaded Channel Estimator for IRS-Aided mmWave Hybrid MIMO System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021439" target="_blank" >RIV/62690094:18450/24:50021439 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10330633" target="_blank" >https://ieeexplore.ieee.org/document/10330633</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LWC.2023.3337289" target="_blank" >10.1109/LWC.2023.3337289</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cascaded Channel Estimator for IRS-Aided mmWave Hybrid MIMO System

Popis výsledku v původním jazyce

The synergistic integration of the intelligent reflecting surface (IRS) and millimeter wave (mmWave) multiple-input multiple-output (MIMO) system is a potential solution for future wireless communication systems, aiming to achieve exceptionally high data rates with enhanced coverage. However, estimation of the cascaded channel state information is essential for beamforming in mmWave MIMO systems with IRS. Unlike conventional MIMO systems, channel estimation for IRS-aided mmWave MIMO systems is challenging due to the limited signal processing capability of the IRS. In this letter, we propose an online sparse exponential forgetting window least mean square-based channel estimator for IRS-assisted mmWave hybrid MIMO systems. Furthermore, we compare accuracy of the proposed estimator with the existing sparse estimators such as orthogonal matching pursuit, sparse Bayesian learning, and oracle least square for benchmarking. Additionally, we perform an analysis of the spectral efficiency and computational complexity of the proposed algorithms. Simulations corroborate superior performance of the proposed method in terms of accuracy, complexity, and robustness.

Název v anglickém jazyce

Cascaded Channel Estimator for IRS-Aided mmWave Hybrid MIMO System

Popis výsledku anglicky

The synergistic integration of the intelligent reflecting surface (IRS) and millimeter wave (mmWave) multiple-input multiple-output (MIMO) system is a potential solution for future wireless communication systems, aiming to achieve exceptionally high data rates with enhanced coverage. However, estimation of the cascaded channel state information is essential for beamforming in mmWave MIMO systems with IRS. Unlike conventional MIMO systems, channel estimation for IRS-aided mmWave MIMO systems is challenging due to the limited signal processing capability of the IRS. In this letter, we propose an online sparse exponential forgetting window least mean square-based channel estimator for IRS-assisted mmWave hybrid MIMO systems. Furthermore, we compare accuracy of the proposed estimator with the existing sparse estimators such as orthogonal matching pursuit, sparse Bayesian learning, and oracle least square for benchmarking. Additionally, we perform an analysis of the spectral efficiency and computational complexity of the proposed algorithms. Simulations corroborate superior performance of the proposed method in terms of accuracy, complexity, and robustness.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Wireless Communications Letters

ISSN

2162-2337

e-ISSN

2162-2345

Svazek periodika

13

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

5

Strana od-do

622-626

Kód UT WoS článku

001184396700012

EID výsledku v databázi Scopus

2-s2.0-85179082193