Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F29142890%3A_____%2F24%3A00048979" target="_blank" >RIV/29142890:_____/24:00048979 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10660298" target="_blank" >https://ieeexplore.ieee.org/document/10660298</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/OJVT.2024.3452412" target="_blank" >10.1109/OJVT.2024.3452412</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System
Original language description
Terahertz (THz) communication is a cutting-edge technology for the sixth-generation (6G) networks, offering vast bandwidths and data rates up to terabits per second, significantly advancing vehicular connectivity and services. However, THz signals are impacted by attenuation, path loss, and beam misalignment. Furthermore, the requisite high Nyquist sampling rates for THz systems increase the computational and system complexity of the receiver. A promising solution to navigate these obstacles involves the use of intelligent reflecting surfaces (IRS)-enhanced multiple-input multiple-output (MIMO) technology, which steers THz wave propagation. However, the substantial dimensions associated with IRS and MIMO extend the near-field, particularly at THz frequencies, as indicated by the Rayleigh distance and suffer from beam squint. To reduce system complexity and reduce sampling to sub-Nyquist rate, we propose a novel receiver design for an IRS-assisted near-field MIMO THz system that employs low-complexity compressed sensing. This method introduces an IRS signal-matched (IRSSM) measurement matrix with beam squint for capturing the transmitted signal at a sub-Nyquist rate, taking advantage of the sparsity in the signal and THz channels, and signal recovery using the deep learning (DL) model. Simulation results for symbol error rate (SER) and normalized mean square error (NMSE) performance indicate that the proposed DL-based receiver outperforms conventional recovery algorithms based on orthogonal matching pursuit (OMP) CS-recovery and dictionary-shrinkage estimation (DSE).
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
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
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 Open Journal of Vehicular Technology
ISSN
2644-1330
e-ISSN
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Volume of the periodical
5
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
1-10
UT code for WoS article
001327401100002
EID of the result in the Scopus database
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