Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks

Jia Jhe Song; Wei Jen Chen; Yung Fang Chen; Shu Ming Tseng

doi:10.1109/ICASI57738.2023.10179541

Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks

Jia Jhe Song, Wei Jen Chen, Yung Fang Chen, Shu Ming Tseng

Department of Communication Engineering

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

Abstract

Previously, we proposed schemes in [1] and [2] for the classical subcarrier, bit, and power allocation problem [3] to minimize the total transmit power for multiuser orthogonal frequency division multiplexing systems in downlink transmission. In this paper, we propose a deep neural network (DNN) structure to speed up solving this complex problem. We propose a deep learning frame structure in which each group of allocation is termed as a batch; after some numbers of iterations and epochs, the loss will tend to converge to a constant value. The simulation results reveal that the proposed DNN-based schemes offer competitive performance and reduce computing time tremendously compared with those of the existing approaches.

Original language	English
Title of host publication	2023 9th International Conference on Applied System Innovation, ICASI 2023
Editors	Shoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	253-255
Number of pages	3
ISBN (Electronic)	9798350398380
DOIs	https://doi.org/10.1109/ICASI57738.2023.10179541
State	Published - 2023
Event	9th International Conference on Applied System Innovation, ICASI 2023 - Chiba, Japan Duration: 21 Apr 2023 → 25 Apr 2023

Publication series

Name	2023 9th International Conference on Applied System Innovation, ICASI 2023

Conference

Conference	9th International Conference on Applied System Innovation, ICASI 2023
Country/Territory	Japan
City	Chiba
Period	21/04/23 → 25/04/23

Keywords

deep learning
deep neural networks
orthogonal frequency division multiple access
resource allocation

Access to Document

10.1109/ICASI57738.2023.10179541

混合式波束成形系統之無線電資源配置演算法設計
Chen, Y.-F. (PI)
1/08/22 → 31/07/23
Project: Research

Cite this

Song, J. J., Chen, W. J., Chen, Y. F., & Tseng, S. M. (2023). Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks. In S.-J. Chang, S.-J. Young, A. D. K.-T. Lam, L.-W. Ji, & S. D. Prior (Eds.), 2023 9th International Conference on Applied System Innovation, ICASI 2023 (pp. 253-255). (2023 9th International Conference on Applied System Innovation, ICASI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI57738.2023.10179541

Song, Jia Jhe ; Chen, Wei Jen ; Chen, Yung Fang et al. / Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks. 2023 9th International Conference on Applied System Innovation, ICASI 2023. editor / Shoou-Jinn Chang ; Sheng-Joue Young ; Artde Donald Kin-Tak Lam ; Liang-Wen Ji ; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 253-255 (2023 9th International Conference on Applied System Innovation, ICASI 2023).

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title = "Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks",

abstract = "Previously, we proposed schemes in [1] and [2] for the classical subcarrier, bit, and power allocation problem [3] to minimize the total transmit power for multiuser orthogonal frequency division multiplexing systems in downlink transmission. In this paper, we propose a deep neural network (DNN) structure to speed up solving this complex problem. We propose a deep learning frame structure in which each group of allocation is termed as a batch; after some numbers of iterations and epochs, the loss will tend to converge to a constant value. The simulation results reveal that the proposed DNN-based schemes offer competitive performance and reduce computing time tremendously compared with those of the existing approaches.",

keywords = "deep learning, deep neural networks, orthogonal frequency division multiple access, resource allocation",

author = "Song, {Jia Jhe} and Chen, {Wei Jen} and Chen, {Yung Fang} and Tseng, {Shu Ming}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Applied System Innovation, ICASI 2023 ; Conference date: 21-04-2023 Through 25-04-2023",

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doi = "10.1109/ICASI57738.2023.10179541",

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series = "2023 9th International Conference on Applied System Innovation, ICASI 2023",

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

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Song, JJ, Chen, WJ, Chen, YF & Tseng, SM 2023, Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks. in S-J Chang, S-J Young, ADK-T Lam, L-W Ji & SD Prior (eds), 2023 9th International Conference on Applied System Innovation, ICASI 2023. 2023 9th International Conference on Applied System Innovation, ICASI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 253-255, 9th International Conference on Applied System Innovation, ICASI 2023, Chiba, Japan, 21/04/23. https://doi.org/10.1109/ICASI57738.2023.10179541

Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks. / Song, Jia Jhe; Chen, Wei Jen; Chen, Yung Fang et al.
2023 9th International Conference on Applied System Innovation, ICASI 2023. ed. / Shoou-Jinn Chang; Sheng-Joue Young; Artde Donald Kin-Tak Lam; Liang-Wen Ji; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2023. p. 253-255 (2023 9th International Conference on Applied System Innovation, ICASI 2023).

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

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T1 - Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks

AU - Song, Jia Jhe

AU - Chen, Wei Jen

AU - Chen, Yung Fang

AU - Tseng, Shu Ming

PY - 2023

Y1 - 2023

N2 - Previously, we proposed schemes in [1] and [2] for the classical subcarrier, bit, and power allocation problem [3] to minimize the total transmit power for multiuser orthogonal frequency division multiplexing systems in downlink transmission. In this paper, we propose a deep neural network (DNN) structure to speed up solving this complex problem. We propose a deep learning frame structure in which each group of allocation is termed as a batch; after some numbers of iterations and epochs, the loss will tend to converge to a constant value. The simulation results reveal that the proposed DNN-based schemes offer competitive performance and reduce computing time tremendously compared with those of the existing approaches.

AB - Previously, we proposed schemes in [1] and [2] for the classical subcarrier, bit, and power allocation problem [3] to minimize the total transmit power for multiuser orthogonal frequency division multiplexing systems in downlink transmission. In this paper, we propose a deep neural network (DNN) structure to speed up solving this complex problem. We propose a deep learning frame structure in which each group of allocation is termed as a batch; after some numbers of iterations and epochs, the loss will tend to converge to a constant value. The simulation results reveal that the proposed DNN-based schemes offer competitive performance and reduce computing time tremendously compared with those of the existing approaches.

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KW - deep neural networks

KW - orthogonal frequency division multiple access

KW - resource allocation

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BT - 2023 9th International Conference on Applied System Innovation, ICASI 2023

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 21 April 2023 through 25 April 2023

ER -

Song JJ, Chen WJ, Chen YF, Tseng SM. Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks. In Chang SJ, Young SJ, Lam ADKT, Ji LW, Prior SD, editors, 2023 9th International Conference on Applied System Innovation, ICASI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 253-255. (2023 9th International Conference on Applied System Innovation, ICASI 2023). doi: 10.1109/ICASI57738.2023.10179541

Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks

Abstract

Publication series

Conference

Keywords

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Projects

混合式波束成形系統之無線電資源配置演算法設計

Cite this