Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network

Di Xu; Xiaojing Chen; Changhao Wu; Shunqing Zhang; Shugong Xu; Shan Cao

doi:10.1109/VTCSpring.2019.8746493

Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network

Di Xu
, Xiaojing Chen
, Changhao Wu
, Shunqing Zhang
, Shugong Xu
, Shan Cao

Shanghai University

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

29 Citations (Scopus)

Abstract

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.

Original language	English
Title of host publication	2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728112176
DOIs	https://doi.org/10.1109/VTCSpring.2019.8746493
Publication status	Published - Apr 2019
Externally published	Yes
Event	89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia Duration: 28 Apr 2019 → 1 May 2019

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2019-April
ISSN (Print)	1550-2252

Conference

Conference	89th IEEE Vehicular Technology Conference, VTC Spring 2019
Country/Territory	Malaysia
City	Kuala Lumpur
Period	28/04/19 → 1/05/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

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10.1109/VTCSpring.2019.8746493

Cite this

Xu, D., Chen, X., Wu, C., Zhang, S., Xu, S., & Cao, S. (2019). Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network. In 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings Article 8746493 (IEEE Vehicular Technology Conference; Vol. 2019-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2019.8746493

@inproceedings{2b795d7b084a4ae99409b04cd87b93e2,

title = "Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network",

abstract = "Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76\% of its CPU runtime.",

author = "Di Xu and Xiaojing Chen and Changhao Wu and Shunqing Zhang and Shugong Xu and Shan Cao",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 89th IEEE Vehicular Technology Conference, VTC Spring 2019 ; Conference date: 28-04-2019 Through 01-05-2019",

year = "2019",

month = apr,

doi = "10.1109/VTCSpring.2019.8746493",

language = "English",

series = "IEEE Vehicular Technology Conference",

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

booktitle = "2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings",

}

Xu, D, Chen, X, Wu, C, Zhang, S, Xu, S & Cao, S 2019, Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network. in 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings., 8746493, IEEE Vehicular Technology Conference, vol. 2019-April, Institute of Electrical and Electronics Engineers Inc., 89th IEEE Vehicular Technology Conference, VTC Spring 2019, Kuala Lumpur, Malaysia, 28/04/19. https://doi.org/10.1109/VTCSpring.2019.8746493

Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network. / Xu, Di; Chen, Xiaojing; Wu, Changhao et al.
2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8746493 (IEEE Vehicular Technology Conference; Vol. 2019-April).

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

TY - GEN

T1 - Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network

AU - Xu, Di

AU - Chen, Xiaojing

AU - Wu, Changhao

AU - Zhang, Shunqing

AU - Xu, Shugong

AU - Cao, Shan

PY - 2019/4

Y1 - 2019/4

N2 - Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.

AB - Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.

UR - https://www.scopus.com/pages/publications/85068979007

U2 - 10.1109/VTCSpring.2019.8746493

DO - 10.1109/VTCSpring.2019.8746493

M3 - Conference Proceeding

AN - SCOPUS:85068979007

T3 - IEEE Vehicular Technology Conference

BT - 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 89th IEEE Vehicular Technology Conference, VTC Spring 2019

Y2 - 28 April 2019 through 1 May 2019

ER -

Energy-efficient subchannel and power allocation for hetnets based on convolutional neural network

Abstract

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UN SDGs

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