Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System
Identifikátory výsledku
Kód výsledku v 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>
Nalezeny alternativní kódy
RIV/29142890:_____/24:00048975
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Adaptive Sparse Channel Estimator for IRS-Assisted mmWave Hybrid MIMO System
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20203 - Telecommunications
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
IEEE Transactions on Cognitive Communications and Networking
ISSN
2332-7731
e-ISSN
2332-7731
Svazek periodika
10
Číslo periodika v rámci svazku
6
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
12
Strana od-do
2224-2235
Kód UT WoS článku
001373834400013
EID výsledku v databázi Scopus
2-s2.0-85197542067