IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection

Taissir Y. Elganimi; Retaj I. Elmajdub; Galymzhan Nauryzbayev; Khaled M. Rabie

doi:10.1109/VTC2022-Fall57202.2022.10012839

IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection

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

^*Corresponding author for this work

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

7 Scopus citations

Abstract

Intelligent reflecting surface (IRS)-assisted beamspace millimeter-wave (mmWave) multiuser massive multiple input multiple output (MIMO) with interference-aware (IA) beam selection scheme is proposed in this paper. This proposed scheme is capable of intelligently reconfiguring the radio environment and utilizing the beam selection for the sake of reducing the number of required radio frequency ( RF) chains without any noticeable performance degradation. To ensure a fair comparison, the achievable sum-rate and energy efficiency (EE) performance metrics of the proposed scheme are evaluated and compared to that of IRS-assisted fully-digital systems with zero-forcing (ZF) precoding and the conventional systems without the IRS technology. Simulation results demonstrate that the proposed IRS-assisted beamspace mmWave massive MIMO system with IA beam selection algorithm outperforms the conventional system without IRS. It is also shown that the performance improves when the number of reflecting elements is more than the total number of mobile users. Moreover, the proposed scheme can potentially offer higher EE than the conventional schemes. Therefore, this shows that the proposed system can be considered as an alternative solution for the future generation of wireless systems.

Original language	English
Title of host publication	2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665454681
DOIs	https://doi.org/10.1109/VTC2022-Fall57202.2022.10012839
State	Published - 2022
Externally published	Yes
Event	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom Duration: 26 Sep 2022 → 29 Sep 2022

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2022-September
ISSN (Print)	1550-2252

Conference

Conference	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/Territory	United Kingdom
City	London
Period	26/09/22 → 29/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Beamspace
IRS
beam selection
massive MIMO
millimeter-wave

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/VTC2022-Fall57202.2022.10012839

Cite this

Elganimi, T. Y., Elmajdub, R. I., Nauryzbayev, G., & Rabie, K. M. (2022). IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings (IEEE Vehicular Technology Conference; Vol. 2022-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012839

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title = "IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection",

abstract = "Intelligent reflecting surface (IRS)-assisted beamspace millimeter-wave (mmWave) multiuser massive multiple input multiple output (MIMO) with interference-aware (IA) beam selection scheme is proposed in this paper. This proposed scheme is capable of intelligently reconfiguring the radio environment and utilizing the beam selection for the sake of reducing the number of required radio frequency ( RF) chains without any noticeable performance degradation. To ensure a fair comparison, the achievable sum-rate and energy efficiency (EE) performance metrics of the proposed scheme are evaluated and compared to that of IRS-assisted fully-digital systems with zero-forcing (ZF) precoding and the conventional systems without the IRS technology. Simulation results demonstrate that the proposed IRS-assisted beamspace mmWave massive MIMO system with IA beam selection algorithm outperforms the conventional system without IRS. It is also shown that the performance improves when the number of reflecting elements is more than the total number of mobile users. Moreover, the proposed scheme can potentially offer higher EE than the conventional schemes. Therefore, this shows that the proposed system can be considered as an alternative solution for the future generation of wireless systems.",

keywords = "Beamspace, IRS, beam selection, massive MIMO, millimeter-wave",

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

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 ; Conference date: 26-09-2022 Through 29-09-2022",

year = "2022",

doi = "10.1109/VTC2022-Fall57202.2022.10012839",

language = "English",

series = "IEEE Vehicular Technology Conference",

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Elganimi, TY, Elmajdub, RI, Nauryzbayev, G & Rabie, KM 2022, IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection. in 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. IEEE Vehicular Technology Conference, vol. 2022-September, Institute of Electrical and Electronics Engineers Inc., 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022, London, United Kingdom, 26/09/22. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012839

IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection. / Elganimi, Taissir Y.; Elmajdub, Retaj I.; Nauryzbayev, Galymzhan et al.
2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE Vehicular Technology Conference; Vol. 2022-September).

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

TY - GEN

T1 - IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection

AU - Elganimi, Taissir Y.

AU - Elmajdub, Retaj I.

AU - Nauryzbayev, Galymzhan

AU - Rabie, Khaled M.

PY - 2022

Y1 - 2022

N2 - Intelligent reflecting surface (IRS)-assisted beamspace millimeter-wave (mmWave) multiuser massive multiple input multiple output (MIMO) with interference-aware (IA) beam selection scheme is proposed in this paper. This proposed scheme is capable of intelligently reconfiguring the radio environment and utilizing the beam selection for the sake of reducing the number of required radio frequency ( RF) chains without any noticeable performance degradation. To ensure a fair comparison, the achievable sum-rate and energy efficiency (EE) performance metrics of the proposed scheme are evaluated and compared to that of IRS-assisted fully-digital systems with zero-forcing (ZF) precoding and the conventional systems without the IRS technology. Simulation results demonstrate that the proposed IRS-assisted beamspace mmWave massive MIMO system with IA beam selection algorithm outperforms the conventional system without IRS. It is also shown that the performance improves when the number of reflecting elements is more than the total number of mobile users. Moreover, the proposed scheme can potentially offer higher EE than the conventional schemes. Therefore, this shows that the proposed system can be considered as an alternative solution for the future generation of wireless systems.

AB - Intelligent reflecting surface (IRS)-assisted beamspace millimeter-wave (mmWave) multiuser massive multiple input multiple output (MIMO) with interference-aware (IA) beam selection scheme is proposed in this paper. This proposed scheme is capable of intelligently reconfiguring the radio environment and utilizing the beam selection for the sake of reducing the number of required radio frequency ( RF) chains without any noticeable performance degradation. To ensure a fair comparison, the achievable sum-rate and energy efficiency (EE) performance metrics of the proposed scheme are evaluated and compared to that of IRS-assisted fully-digital systems with zero-forcing (ZF) precoding and the conventional systems without the IRS technology. Simulation results demonstrate that the proposed IRS-assisted beamspace mmWave massive MIMO system with IA beam selection algorithm outperforms the conventional system without IRS. It is also shown that the performance improves when the number of reflecting elements is more than the total number of mobile users. Moreover, the proposed scheme can potentially offer higher EE than the conventional schemes. Therefore, this shows that the proposed system can be considered as an alternative solution for the future generation of wireless systems.

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KW - millimeter-wave

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M3 - Conference contribution

AN - SCOPUS:85147015262

T3 - IEEE Vehicular Technology Conference

BT - 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022

Y2 - 26 September 2022 through 29 September 2022

ER -

Elganimi TY, Elmajdub RI, Nauryzbayev G, Rabie KM. IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (IEEE Vehicular Technology Conference). doi: 10.1109/VTC2022-Fall57202.2022.10012839

IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this