Performance Analysis of Sparse Channel Estimators for Millimeter Wave Hybrid MIMO Systems with Non-Ideal Hardware

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020430" target="_blank" >RIV/62690094:18450/23:50020430 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10109185/" target="_blank" >https://ieeexplore.ieee.org/document/10109185/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TVT.2023.3270240" target="_blank" >10.1109/TVT.2023.3270240</a>

Result language

angličtina

Original language name

Performance Analysis of Sparse Channel Estimators for Millimeter Wave Hybrid MIMO Systems with Non-Ideal Hardware

Original language description

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) is the state-of-the-art physical layer technique for the fifth and beyond fifth-generation (5G/B5G) wireless communication systems. However, existing works in mmWave hybrid (analog and digital) MIMO systems do not adequately address the impact of unavoidable residual transceiver hardware impairments (HIs). This paper, considers a mmWave hybrid MIMO system with residual HIs and estimates the channel of considered system in a downlink scenario. The residual transceiver HIs are modeled as additive distortion noise, that severely affects the received pilot and information signals, which makes channel estimation a challenging task. As distortion noise is non-stationary, hence, an online adaptive filtering-based zero-attracting least mean square (ZALMS) is proposed. To ensure a lower mean square error the range of step-size and regularization parameters are obtained. Further, to achieve a faster convergence rate a sparse-initiated ZALMS (SI-ZALMS) is proposed. Furthermore, the impact of HIs on the mean square deviation and spectral efficiency is also analyzed. The proposed method offers significantly lower computational complexity as compared with the existing sparse channel estimation methods like Bayesian compressive sensing and sparse Bayesian learning. Simulation and analytical results corroborate the superiority of the proposed method as compared with existing methods. IEEE

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

IEEE Transactions on Vehicular Technology

ISSN

0018-9545

e-ISSN

1939-9359

Volume of the periodical

72

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

11913-11923

UT code for WoS article

001103676800064

EID of the result in the Scopus database

2-s2.0-85159706469