Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System
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 mis...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10660298/ |
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| author | Vaishali Sharma Prakhar Keshari Sanjeev Sharma Kuntal Deka Ondrej Krejcar Vimal Bhatia |
| author_facet | Vaishali Sharma Prakhar Keshari Sanjeev Sharma Kuntal Deka Ondrej Krejcar Vimal Bhatia |
| author_sort | Vaishali Sharma |
| collection | DOAJ |
| 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). |
| format | Article |
| id | doaj-art-34a08e24ee5a4a2fb0777608262dd8aa |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-34a08e24ee5a4a2fb0777608262dd8aa2025-01-30T00:04:14ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151326133510.1109/OJVT.2024.345241210660298Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO SystemVaishali Sharma0https://orcid.org/0000-0001-8126-0506Prakhar Keshari1Sanjeev Sharma2Kuntal Deka3https://orcid.org/0000-0002-8782-1682Ondrej Krejcar4https://orcid.org/0000-0002-5992-2574Vimal Bhatia5https://orcid.org/0000-0001-5148-6643Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Indore, Indore, IndiaDepartment of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, IndiaDepartment of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, IndiaSkoda Auto University, Mlada Boleslav, Czech RepublicDepartment of Electrical Engineering, Indian Institute of Technology Indore, Indore, IndiaTerahertz (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).https://ieeexplore.ieee.org/document/10660298/THz bandsymbol detectioncompressed sensingMIMODNNnear-field |
| spellingShingle | Vaishali Sharma Prakhar Keshari Sanjeev Sharma Kuntal Deka Ondrej Krejcar Vimal Bhatia Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System IEEE Open Journal of Vehicular Technology THz band symbol detection compressed sensing MIMO DNN near-field |
| title | Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System |
| title_full | Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System |
| title_fullStr | Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System |
| title_full_unstemmed | Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System |
| title_short | Deep Learning Model for CS-Based Signal Recovery for IRS-Assisted Near-Field THz MIMO System |
| title_sort | deep learning model for cs based signal recovery for irs assisted near field thz mimo system |
| topic | THz band symbol detection compressed sensing MIMO DNN near-field |
| url | https://ieeexplore.ieee.org/document/10660298/ |
| work_keys_str_mv | AT vaishalisharma deeplearningmodelforcsbasedsignalrecoveryforirsassistednearfieldthzmimosystem AT prakharkeshari deeplearningmodelforcsbasedsignalrecoveryforirsassistednearfieldthzmimosystem AT sanjeevsharma deeplearningmodelforcsbasedsignalrecoveryforirsassistednearfieldthzmimosystem AT kuntaldeka deeplearningmodelforcsbasedsignalrecoveryforirsassistednearfieldthzmimosystem AT ondrejkrejcar deeplearningmodelforcsbasedsignalrecoveryforirsassistednearfieldthzmimosystem AT vimalbhatia deeplearningmodelforcsbasedsignalrecoveryforirsassistednearfieldthzmimosystem |