Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming
Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency (EE) of massive multiple-input multiple-output (mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to t...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10574840/ |
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| author | Hamed Hojatian Zoubeir Mlika Jeremy Nadal Jean-Francois Frigon Francois Leduc-Primeau |
| author_facet | Hamed Hojatian Zoubeir Mlika Jeremy Nadal Jean-Francois Frigon Francois Leduc-Primeau |
| author_sort | Hamed Hojatian |
| collection | DOAJ |
| description | Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency (EE) of massive multiple-input multiple-output (mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder (FDP) and HBF transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop a self-supervised learning (SSL) method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, towards obtaining a system that can be trained using in-the-field measurements, we investigate the ability of the model to be trained exclusively using imperfect channel state information (CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods. |
| format | Article |
| id | doaj-art-5b7b7cec246743d8bbfffa2e627195cb |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-5b7b7cec246743d8bbfffa2e627195cb2025-08-20T02:53:09ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01293995510.1109/TMLCN.2024.341972810574840Learning Energy-Efficient Transmitter Configurations for Massive MIMO BeamformingHamed Hojatian0https://orcid.org/0000-0002-6676-0630Zoubeir Mlika1Jeremy Nadal2https://orcid.org/0000-0001-8720-525XJean-Francois Frigon3https://orcid.org/0000-0002-3622-137XFrancois Leduc-Primeau4https://orcid.org/0000-0002-5528-8510Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC, CanadaDepartment of Electrical Engineering, Polytechnique Montreal, Montreal, QC, CanadaDepartment of Mathematical and Electrical Engineering, IMT Atlantique, Nantes, FranceDepartment of Electrical Engineering, Polytechnique Montreal, Montreal, QC, CanadaDepartment of Electrical Engineering, Polytechnique Montreal, Montreal, QC, CanadaHybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency (EE) of massive multiple-input multiple-output (mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder (FDP) and HBF transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop a self-supervised learning (SSL) method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, towards obtaining a system that can be trained using in-the-field measurements, we investigate the ability of the model to be trained exclusively using imperfect channel state information (CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods.https://ieeexplore.ieee.org/document/10574840/Beamformingdeep neural networkenergy efficiencyfully digital beamforminghybrid beamformingmassive MIMO |
| spellingShingle | Hamed Hojatian Zoubeir Mlika Jeremy Nadal Jean-Francois Frigon Francois Leduc-Primeau Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming IEEE Transactions on Machine Learning in Communications and Networking Beamforming deep neural network energy efficiency fully digital beamforming hybrid beamforming massive MIMO |
| title | Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming |
| title_full | Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming |
| title_fullStr | Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming |
| title_full_unstemmed | Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming |
| title_short | Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming |
| title_sort | learning energy efficient transmitter configurations for massive mimo beamforming |
| topic | Beamforming deep neural network energy efficiency fully digital beamforming hybrid beamforming massive MIMO |
| url | https://ieeexplore.ieee.org/document/10574840/ |
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