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Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach

Research output: Contribution to journalArticle

Standard

Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach. / Jia, Guangyu; Yang, Zhaohui; Lam, Hak Keung; Shi, Jianfeng; Shikh-Bahaei, Mohammad.

In: IEEE COMMUNICATIONS LETTERS, Vol. 24, No. 4, 8967053, 04.2020, p. 787-791.

Research output: Contribution to journalArticle

Harvard

Jia, G, Yang, Z, Lam, HK, Shi, J & Shikh-Bahaei, M 2020, 'Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach', IEEE COMMUNICATIONS LETTERS, vol. 24, no. 4, 8967053, pp. 787-791. https://doi.org/10.1109/LCOMM.2020.2968902

APA

Jia, G., Yang, Z., Lam, H. K., Shi, J., & Shikh-Bahaei, M. (2020). Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach. IEEE COMMUNICATIONS LETTERS, 24(4), 787-791. [8967053]. https://doi.org/10.1109/LCOMM.2020.2968902

Vancouver

Jia G, Yang Z, Lam HK, Shi J, Shikh-Bahaei M. Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach. IEEE COMMUNICATIONS LETTERS. 2020 Apr;24(4):787-791. 8967053. https://doi.org/10.1109/LCOMM.2020.2968902

Author

Jia, Guangyu ; Yang, Zhaohui ; Lam, Hak Keung ; Shi, Jianfeng ; Shikh-Bahaei, Mohammad. / Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach. In: IEEE COMMUNICATIONS LETTERS. 2020 ; Vol. 24, No. 4. pp. 787-791.

Bibtex Download

@article{c7d92fcde089462493bffe2846a2f6a2,
title = "Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach",
abstract = "This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.",
keywords = "convex optimization, deep learning, machine learning, Resource allocation",
author = "Guangyu Jia and Zhaohui Yang and Lam, {Hak Keung} and Jianfeng Shi and Mohammad Shikh-Bahaei",
year = "2020",
month = apr,
doi = "10.1109/LCOMM.2020.2968902",
language = "English",
volume = "24",
pages = "787--791",
journal = "IEEE COMMUNICATIONS LETTERS",
issn = "1089-7798",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach

AU - Jia, Guangyu

AU - Yang, Zhaohui

AU - Lam, Hak Keung

AU - Shi, Jianfeng

AU - Shikh-Bahaei, Mohammad

PY - 2020/4

Y1 - 2020/4

N2 - This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.

AB - This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.

KW - convex optimization

KW - deep learning

KW - machine learning

KW - Resource allocation

UR - http://www.scopus.com/inward/record.url?scp=85083266559&partnerID=8YFLogxK

U2 - 10.1109/LCOMM.2020.2968902

DO - 10.1109/LCOMM.2020.2968902

M3 - Article

AN - SCOPUS:85083266559

VL - 24

SP - 787

EP - 791

JO - IEEE COMMUNICATIONS LETTERS

JF - IEEE COMMUNICATIONS LETTERS

SN - 1089-7798

IS - 4

M1 - 8967053

ER -

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