Machine Learning-Based Generalized User Grouping in NOMA

Weichao Chen; Shengjie Zhao; Rongqing Zhang; Yi Chen; Liuqing Yang

doi:10.1109/GLOBECOM42002.2020.9322462

Machine Learning-Based Generalized User Grouping in NOMA

Weichao Chen, Shengjie Zhao, Rongqing Zhang, Yi Chen, Liuqing Yang

Electrical and Computer Engineering

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

3 Scopus citations

Abstract

Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.

Original language	English (US)
Title of host publication	2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728182988
DOIs	https://doi.org/10.1109/GLOBECOM42002.2020.9322462
State	Published - Dec 2020
Event	2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China Duration: Dec 7 2020 → Dec 11 2020

Publication series

Name	2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

Conference	2020 IEEE Global Communications Conference, GLOBECOM 2020
Country/Territory	Taiwan, Province of China
City	Virtual, Taipei
Period	12/7/20 → 12/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

NOMA
generalized user grouping
machine learning
overlapping
power control

Publisher link

10.1109/GLOBECOM42002.2020.9322462

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Chen, W., Zhao, S., Zhang, R., Chen, Y., & Yang, L. (2020). Machine Learning-Based Generalized User Grouping in NOMA. In 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings [9322462] (2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOBECOM42002.2020.9322462

Machine Learning-Based Generalized User Grouping in NOMA. / Chen, Weichao; Zhao, Shengjie; Zhang, Rongqing et al.

2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9322462 (2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings).

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

Chen, W, Zhao, S, Zhang, R, Chen, Y & Yang, L 2020, Machine Learning-Based Generalized User Grouping in NOMA. in 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings., 9322462, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, Taiwan, Province of China, 12/7/20. https://doi.org/10.1109/GLOBECOM42002.2020.9322462

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abstract = "Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.",

keywords = "NOMA, generalized user grouping, machine learning, overlapping, power control",

author = "Weichao Chen and Shengjie Zhao and Rongqing Zhang and Yi Chen and Liuqing Yang",

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AU - Zhao, Shengjie

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N2 - Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.

AB - Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.

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KW - overlapping

KW - power control

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M3 - Conference contribution

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ER -

Machine Learning-Based Generalized User Grouping in NOMA

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