Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems

Jia Jhe Song; Yung Fang Chen

doi:10.1109/ICASI57738.2023.10179573

Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems

Jia Jhe Song, Yung Fang Chen

Department of Communication Engineering

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

Abstract

In this paper, we apply deep learning-based (DL) approach to solve the hybrid precoding and combining design problem in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. After training process, we feed testing data set into neural network (NN) and obtain phases of RF analog precoders and combiners. Given a RF analog precoder, we can acquire baseband precoders by using least square solution and the similar way is applied to RF analog combiner to acquire baseband combiner. As indicated in the simulation results for the evaluated spectral efficiency based on the outputs of DNN, it shows that the performance of our method is competitive.

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	250-252
Number of pages	3
ISBN (Electronic)	9798350398380
DOIs	https://doi.org/10.1109/ICASI57738.2023.10179573
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.
hybrid beamforming
multiple-input multiple-output

Access to Document

10.1109/ICASI57738.2023.10179573

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, Y. F. (2023). Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems. 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. 250-252). (2023 9th International Conference on Applied System Innovation, ICASI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI57738.2023.10179573

Song, Jia Jhe ; Chen, Yung Fang. / Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems. 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. 250-252 (2023 9th International Conference on Applied System Innovation, ICASI 2023).

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abstract = "In this paper, we apply deep learning-based (DL) approach to solve the hybrid precoding and combining design problem in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. After training process, we feed testing data set into neural network (NN) and obtain phases of RF analog precoders and combiners. Given a RF analog precoder, we can acquire baseband precoders by using least square solution and the similar way is applied to RF analog combiner to acquire baseband combiner. As indicated in the simulation results for the evaluated spectral efficiency based on the outputs of DNN, it shows that the performance of our method is competitive.",

keywords = "deep learning, deep neural networks., hybrid beamforming, multiple-input multiple-output",

author = "Song, {Jia Jhe} and Chen, {Yung Fang}",

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Song, JJ & Chen, YF 2023, Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems. 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. 250-252, 9th International Conference on Applied System Innovation, ICASI 2023, Chiba, Japan, 21/04/23. https://doi.org/10.1109/ICASI57738.2023.10179573

Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems. / Song, Jia Jhe; Chen, Yung Fang.
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. 250-252 (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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N2 - In this paper, we apply deep learning-based (DL) approach to solve the hybrid precoding and combining design problem in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. After training process, we feed testing data set into neural network (NN) and obtain phases of RF analog precoders and combiners. Given a RF analog precoder, we can acquire baseband precoders by using least square solution and the similar way is applied to RF analog combiner to acquire baseband combiner. As indicated in the simulation results for the evaluated spectral efficiency based on the outputs of DNN, it shows that the performance of our method is competitive.

AB - In this paper, we apply deep learning-based (DL) approach to solve the hybrid precoding and combining design problem in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. After training process, we feed testing data set into neural network (NN) and obtain phases of RF analog precoders and combiners. Given a RF analog precoder, we can acquire baseband precoders by using least square solution and the similar way is applied to RF analog combiner to acquire baseband combiner. As indicated in the simulation results for the evaluated spectral efficiency based on the outputs of DNN, it shows that the performance of our method is competitive.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Song JJ, Chen YF. Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems. 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. 250-252. (2023 9th International Conference on Applied System Innovation, ICASI 2023). doi: 10.1109/ICASI57738.2023.10179573

Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems

Abstract

Publication series

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Keywords

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混合式波束成形系統之無線電資源配置演算法設計

Cite this