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Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD

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Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD. / Boukley Hasan, Wael; Harris, Paul; Bromell, Henry; Doufexi, Angela; Beach, Mark.

In: IEEE Access, Vol. 7, 04.11.2019, p. 162683 - 162696.

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@article{0d556627f59d48e8a23ba212fa414192,
title = "Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD",
abstract = "Massive multiple-input, multiple-output (Ma-MIMO) offers significant capacity improvements for sub6GHz wireless access. Evaluating the practicalities for real-world deployments and identifying solutions is the next critical step in the roll-out of this technology. Here novel adaptive user grouping algorithms for a single cell Ma-MIMO scenario are proposed and shown to further enhance performance. For the first time, the methodology addresses the interference caused by the user channel vectors as well as hardware impairments. Here, this is uniquely achieved by extracting the Error Vector Magnitude (EVM), and three different methodologies are applied to address specific wireless connectivity objectives. The first, maximizes the spectral efficiency which is more desirable for cellular networks. The second, maximizes the number of users for low data rate bearers, which is suitable for the Internet of Things (IoT). Whilst, the third approach focuses on specific quality per user, for applications such as wireless cameras at major cultural or sporting events. Importantly, real-world experimental data-sets have been used to evaluate the proposed user grouping algorithms.",
keywords = "Massive MIMO, 5G, User Grouping, Channel Estimation, EVM Prediction, SINR Estimation, Quality of Service",
author = "{Boukley Hasan}, Wael and Paul Harris and Henry Bromell and Angela Doufexi and Mark Beach",
year = "2019",
month = "11",
day = "4",
doi = "10.1109/ACCESS.2019.2951547",
language = "English",
volume = "7",
pages = "162683 -- 162696",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

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TY - JOUR

T1 - Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD

AU - Boukley Hasan, Wael

AU - Harris, Paul

AU - Bromell, Henry

AU - Doufexi, Angela

AU - Beach, Mark

PY - 2019/11/4

Y1 - 2019/11/4

N2 - Massive multiple-input, multiple-output (Ma-MIMO) offers significant capacity improvements for sub6GHz wireless access. Evaluating the practicalities for real-world deployments and identifying solutions is the next critical step in the roll-out of this technology. Here novel adaptive user grouping algorithms for a single cell Ma-MIMO scenario are proposed and shown to further enhance performance. For the first time, the methodology addresses the interference caused by the user channel vectors as well as hardware impairments. Here, this is uniquely achieved by extracting the Error Vector Magnitude (EVM), and three different methodologies are applied to address specific wireless connectivity objectives. The first, maximizes the spectral efficiency which is more desirable for cellular networks. The second, maximizes the number of users for low data rate bearers, which is suitable for the Internet of Things (IoT). Whilst, the third approach focuses on specific quality per user, for applications such as wireless cameras at major cultural or sporting events. Importantly, real-world experimental data-sets have been used to evaluate the proposed user grouping algorithms.

AB - Massive multiple-input, multiple-output (Ma-MIMO) offers significant capacity improvements for sub6GHz wireless access. Evaluating the practicalities for real-world deployments and identifying solutions is the next critical step in the roll-out of this technology. Here novel adaptive user grouping algorithms for a single cell Ma-MIMO scenario are proposed and shown to further enhance performance. For the first time, the methodology addresses the interference caused by the user channel vectors as well as hardware impairments. Here, this is uniquely achieved by extracting the Error Vector Magnitude (EVM), and three different methodologies are applied to address specific wireless connectivity objectives. The first, maximizes the spectral efficiency which is more desirable for cellular networks. The second, maximizes the number of users for low data rate bearers, which is suitable for the Internet of Things (IoT). Whilst, the third approach focuses on specific quality per user, for applications such as wireless cameras at major cultural or sporting events. Importantly, real-world experimental data-sets have been used to evaluate the proposed user grouping algorithms.

KW - Massive MIMO

KW - 5G

KW - User Grouping

KW - Channel Estimation

KW - EVM Prediction

KW - SINR Estimation

KW - Quality of Service

U2 - 10.1109/ACCESS.2019.2951547

DO - 10.1109/ACCESS.2019.2951547

M3 - Article

VL - 7

SP - 162683

EP - 162696

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -