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Unsupervised Deep Learning-Based Hybrid Beamforming in Massive MISO Systems

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Wireless Algorithms, Systems, and Applications (WASA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13472))

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Abstract

Hybrid beamforming (HBF) is a promising approach for balancing the hardware cost, training overhead and system performance in massive MIMO systems. Optimizing the HBF through deep learning (DL) has gained considerable attention in recent years due to its potential in dealing with the nonconvex problems. However, existing DL-based HBF methods require wider or deeper neural networks to guarantee training performance, which not only leads to higher complexity in training and deploying, but also increases the risk of over-fitting. In this paper, we propose a low-complexity HBF method based on convolutional neural network (CNN) to solve the spectral efficiency (SE) maximization problem with constant modulus constraint for the analog phase shifters over the transmit power budget in a multiple-input single-output (MISO) system. An unsupervised learning strategy is derived for the constructed CNN to learn to generate feasible beamforming solutions adaptively and thus avoiding any label data when training them. Simulations show its advantages in both SE and complexity over other related algorithms.

This work was supported in part by the National Key R &D Program of China under Grant 2019YFB2102600, the National Natural Science Foundation of China (NSFC) under Grants 61701269, 61832012 and 61771289, and the Shandong Provincial Natural Science Foundation under Grant ZR2021MF026; the Fundamental Research Enhancement Program for Computer Science and Technology, the Talent Cultivation Promotion Program for Computer Science and Technology, and the Piloting Fundamental Research Program for the Integration of Scientific Research, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) under Grants 2021JC02014, 2021PY05001 and 2022XD001, respectively.

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

    Teng Zhang, Anming Dong, Sufang Li & Li Zhang

  2. Big Data Institute and School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

    Anming Dong, Jiguo Yu & Li Zhang

  3. Department of Electrical and Electronic Engineering, University of Bristol, Bristol, BS8 1UB, UK

    Chuanting Zhang

  4. School of Mathematical Science, Qufu Normal University, Qufu, 273100, China

    Jing Qiu

  5. Shandong HiCon New Media Institute Co., Ltd., Jinan, China

    You Zhou

Authors
  1. Teng Zhang
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  2. Anming Dong
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  3. Chuanting Zhang
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  4. Jiguo Yu
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  5. Jing Qiu
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  6. Sufang Li
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  7. Li Zhang
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  8. You Zhou
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Corresponding author

Correspondence to Anming Dong .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Lei Wang

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Michael Segal

  3. Chang Gung University, Taiwan, China

    Jenhui Chen

  4. Tianjin University, Tianjin, China

    Tie Qiu

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Zhang, T. et al. (2022). Unsupervised Deep Learning-Based Hybrid Beamforming in Massive MISO Systems. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-19214-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19213-5

  • Online ISBN: 978-3-031-19214-2

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