Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect

This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivit...

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Main Authors: Priyanka Maity, Suraj Srivastava, Sunaina Khatri, Aditya K. Jagannatham
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9932583/
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author Priyanka Maity
Suraj Srivastava
Sunaina Khatri
Aditya K. Jagannatham
author_facet Priyanka Maity
Suraj Srivastava
Sunaina Khatri
Aditya K. Jagannatham
author_sort Priyanka Maity
collection DOAJ
description This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems. To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)-based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the non-idealities.
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spelling doaj.art-049dd417a31b4a2a805ee1b69efd1c532022-12-22T02:27:58ZengIEEEIEEE Access2169-35362022-01-011011369911371410.1109/ACCESS.2022.32180329932583Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint EffectPriyanka Maity0https://orcid.org/0000-0001-7542-0999Suraj Srivastava1https://orcid.org/0000-0002-5793-6040Sunaina Khatri2Aditya K. Jagannatham3https://orcid.org/0000-0003-1594-5181Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaQualcomm, Bengaluru, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaThis work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems. To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)-based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the non-idealities.https://ieeexplore.ieee.org/document/9932583/Dictionary learning (DL)TerahertzMIMOchannel estimationhardware impairmentsarray calibration
spellingShingle Priyanka Maity
Suraj Srivastava
Sunaina Khatri
Aditya K. Jagannatham
Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect
IEEE Access
Dictionary learning (DL)
Terahertz
MIMO
channel estimation
hardware impairments
array calibration
title Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect
title_full Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect
title_fullStr Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect
title_full_unstemmed Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect
title_short Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect
title_sort dictionary learning dl based sparse csi estimation in multiuser terahertz thz hybrid mimo systems under hardware impairments and beam squint effect
topic Dictionary learning (DL)
Terahertz
MIMO
channel estimation
hardware impairments
array calibration
url https://ieeexplore.ieee.org/document/9932583/
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AT surajsrivastava dictionarylearningdlbasedsparsecsiestimationinmultiuserterahertzthzhybridmimosystemsunderhardwareimpairmentsandbeamsquinteffect
AT sunainakhatri dictionarylearningdlbasedsparsecsiestimationinmultiuserterahertzthzhybridmimosystemsunderhardwareimpairmentsandbeamsquinteffect
AT adityakjagannatham dictionarylearningdlbasedsparsecsiestimationinmultiuserterahertzthzhybridmimosystemsunderhardwareimpairmentsandbeamsquinteffect