IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks

Taissir Y. Elganimi; Khaled M. Rabie; Galymzhan Nauryzbayev

doi:10.1109/VTC2023-Spring57618.2023.10199562

IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks

Taissir Y. Elganimi^*, Khaled M. Rabie, Galymzhan Nauryzbayev

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

An intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system with transmit antenna selection (TAS) using orthogonal space-time block codes (OSTBC) scheme is proposed in this paper. This system combines TAS and IRS with hybrid analog-digital beamforming (HBF) for 60 GHz mmWave communications in order to exploit the benefits of TAS, OSTBC, analog beamforming (ABF), and transmit digital precoding techniques. The proposed system, however, benefits from the transmit diversity gain of OSTBC scheme as well as from the signal-to-noise ratio (SNR) gains of both the beamformer and the IRS technology. The simulation results show that TAS-OSTBC system with zero-forcing precoding outperforms the conventional TAS-OSTBC scheme. Furthermore, the bit error rate (BER) performance significantly improves as the number of antenna array elements increases due to providing a beamforming gain. In addition, increasing the number of reflecting elements further enhances the BER performance. It is also found from the simulation results that the TAS-OSTBC system with hybrid precoding has better performance than that of TAS-OSTBC with ABF, and IRS-assisted systems significantly outperform the conventional systems without the IRS technology. This makes the proposed IRS-assisted system an appealing solution for internet-of-things (IoT) networks.

Original language	English
Title of host publication	2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350311143
DOIs	https://doi.org/10.1109/VTC2023-Spring57618.2023.10199562
State	Published - 2023
Externally published	Yes
Event	97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy Duration: 20 Jun 2023 → 23 Jun 2023

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2023-June
ISSN (Print)	1550-2252

Conference

Conference	97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/Territory	Italy
City	Florence
Period	20/06/23 → 23/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Intelligent reflecting surface (IRS)
OSTBC
millimeter-wave massive MIMO
transmit antenna selection (TAS)

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/VTC2023-Spring57618.2023.10199562

Cite this

Elganimi, T. Y., Rabie, K. M., & Nauryzbayev, G. (2023). IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks. In 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings (IEEE Vehicular Technology Conference; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199562

@inproceedings{32042fa1e918485480632b22d8620826,

title = "IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks",

abstract = "An intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system with transmit antenna selection (TAS) using orthogonal space-time block codes (OSTBC) scheme is proposed in this paper. This system combines TAS and IRS with hybrid analog-digital beamforming (HBF) for 60 GHz mmWave communications in order to exploit the benefits of TAS, OSTBC, analog beamforming (ABF), and transmit digital precoding techniques. The proposed system, however, benefits from the transmit diversity gain of OSTBC scheme as well as from the signal-to-noise ratio (SNR) gains of both the beamformer and the IRS technology. The simulation results show that TAS-OSTBC system with zero-forcing precoding outperforms the conventional TAS-OSTBC scheme. Furthermore, the bit error rate (BER) performance significantly improves as the number of antenna array elements increases due to providing a beamforming gain. In addition, increasing the number of reflecting elements further enhances the BER performance. It is also found from the simulation results that the TAS-OSTBC system with hybrid precoding has better performance than that of TAS-OSTBC with ABF, and IRS-assisted systems significantly outperform the conventional systems without the IRS technology. This makes the proposed IRS-assisted system an appealing solution for internet-of-things (IoT) networks.",

keywords = "Intelligent reflecting surface (IRS), OSTBC, millimeter-wave massive MIMO, transmit antenna selection (TAS)",

author = "Elganimi, \{Taissir Y.\} and Rabie, \{Khaled M.\} and Galymzhan Nauryzbayev",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 97th IEEE Vehicular Technology Conference, VTC 2023-Spring ; Conference date: 20-06-2023 Through 23-06-2023",

year = "2023",

doi = "10.1109/VTC2023-Spring57618.2023.10199562",

language = "English",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings",

address = "United States",

}

Elganimi, TY, Rabie, KM & Nauryzbayev, G 2023, IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks. in 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. IEEE Vehicular Technology Conference, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 97th IEEE Vehicular Technology Conference, VTC 2023-Spring, Florence, Italy, 20/06/23. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199562

IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks. / Elganimi, Taissir Y.; Rabie, Khaled M.; Nauryzbayev, Galymzhan.
2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE Vehicular Technology Conference; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks

AU - Elganimi, Taissir Y.

AU - Rabie, Khaled M.

AU - Nauryzbayev, Galymzhan

PY - 2023

Y1 - 2023

N2 - An intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system with transmit antenna selection (TAS) using orthogonal space-time block codes (OSTBC) scheme is proposed in this paper. This system combines TAS and IRS with hybrid analog-digital beamforming (HBF) for 60 GHz mmWave communications in order to exploit the benefits of TAS, OSTBC, analog beamforming (ABF), and transmit digital precoding techniques. The proposed system, however, benefits from the transmit diversity gain of OSTBC scheme as well as from the signal-to-noise ratio (SNR) gains of both the beamformer and the IRS technology. The simulation results show that TAS-OSTBC system with zero-forcing precoding outperforms the conventional TAS-OSTBC scheme. Furthermore, the bit error rate (BER) performance significantly improves as the number of antenna array elements increases due to providing a beamforming gain. In addition, increasing the number of reflecting elements further enhances the BER performance. It is also found from the simulation results that the TAS-OSTBC system with hybrid precoding has better performance than that of TAS-OSTBC with ABF, and IRS-assisted systems significantly outperform the conventional systems without the IRS technology. This makes the proposed IRS-assisted system an appealing solution for internet-of-things (IoT) networks.

AB - An intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system with transmit antenna selection (TAS) using orthogonal space-time block codes (OSTBC) scheme is proposed in this paper. This system combines TAS and IRS with hybrid analog-digital beamforming (HBF) for 60 GHz mmWave communications in order to exploit the benefits of TAS, OSTBC, analog beamforming (ABF), and transmit digital precoding techniques. The proposed system, however, benefits from the transmit diversity gain of OSTBC scheme as well as from the signal-to-noise ratio (SNR) gains of both the beamformer and the IRS technology. The simulation results show that TAS-OSTBC system with zero-forcing precoding outperforms the conventional TAS-OSTBC scheme. Furthermore, the bit error rate (BER) performance significantly improves as the number of antenna array elements increases due to providing a beamforming gain. In addition, increasing the number of reflecting elements further enhances the BER performance. It is also found from the simulation results that the TAS-OSTBC system with hybrid precoding has better performance than that of TAS-OSTBC with ABF, and IRS-assisted systems significantly outperform the conventional systems without the IRS technology. This makes the proposed IRS-assisted system an appealing solution for internet-of-things (IoT) networks.

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KW - transmit antenna selection (TAS)

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DO - 10.1109/VTC2023-Spring57618.2023.10199562

M3 - Conference contribution

AN - SCOPUS:85169784078

T3 - IEEE Vehicular Technology Conference

BT - 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 97th IEEE Vehicular Technology Conference, VTC 2023-Spring

Y2 - 20 June 2023 through 23 June 2023

ER -

IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this