Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021954" target="_blank" >RIV/62690094:18450/24:50021954 - isvavai.cz</a>
Alternative codes found
RIV/29142890:_____/24:00048975
Result on the web
<a href="https://ieeexplore.ieee.org/document/10584088" target="_blank" >https://ieeexplore.ieee.org/document/10584088</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCCN.2024.3422510" target="_blank" >10.1109/TCCN.2024.3422510</a>
Alternative languages
Result language
angličtina
Original language name
Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System
Original language description
A viable technology for the future wireless communication system to obtain extremely high information rates with improved coverage is the collaborative incorporation of an intelligent reflecting surface (IRS) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. An IRS provides a virtual line-of-sight (LoS) path to enhance the wireless system's capacity. However, accurate channel state information is essential for the complete utilization of IRS and mmWave MIMO systems. Existing channel estimators based on orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL) entail large pilot overhead and matrix inversion. Therefore, these techniques offer low spectral efficiency and high computational complexity. To overcome the limitations of existing estimators, we propose an online variable step-size zero-attracting least mean square (VSS-ZALMS) based algorithm for IRS-assisted mmWave hybrid MIMO system channel estimation. Further, we derive analytical expressions for the range of step-size and regularization parameters to improve estimation accuracy and convergence rates. Moreover, we conduct an analysis of IRS location, spectral efficiency, complexity analysis, and pilot overhead requirements. Simulation results are then compared with OMP, SBL, and oracle least square for benchmarking. The results corroborate superiority of the proposed approach concerning accuracy, complexity, and robustness compared to the existing estimators.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Cognitive Communications and Networking
ISSN
2332-7731
e-ISSN
2332-7731
Volume of the periodical
10
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
2224-2235
UT code for WoS article
001373834400013
EID of the result in the Scopus database
2-s2.0-85197542067