MDL-AltMin: A Hybrid Precoding Scheme for mmWave Systems With Deep Learning and Alternate Optimization
The hybrid precoding structure composed of analog and digital precoders has received increasing attention in mmWave massive multiple-input multiple-output (MIMO) systems because it can balance the energy consumption and spectral efficiency (SE). However, it is challenging to obtain the optimal hybri...
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| Veröffentlicht in: | IEEE wireless communications letters Jg. 11; H. 9; S. 1925 - 1929 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Piscataway
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-2337, 2162-2345 |
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| Abstract | The hybrid precoding structure composed of analog and digital precoders has received increasing attention in mmWave massive multiple-input multiple-output (MIMO) systems because it can balance the energy consumption and spectral efficiency (SE). However, it is challenging to obtain the optimal hybrid precoding scheme by joint optimization with lower computational complexity. This letter proposes a hybrid precoding scheme based on model-driven deep learning and alternate minimization (MDL-AltMin), which is implemented by alternately solving analog precoder and digital precoder. During the alternation, we design an analog precoding network (AP-Net) to solve the phase shift network in analog precoder with the goal of maximizing SE. The digital precoder is solved by the Lagrange multiplier method. In each alternate optimization process, the criteria for convergence is to minimize the error between the hybrid precoder and the optimal fully digital precoder. The simulation results show that the SE of our proposed scheme is very close to the fully digital precoding scheme based on singular value decomposition with lower computational complexity. |
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| AbstractList | The hybrid precoding structure composed of analog and digital precoders has received increasing attention in mmWave massive multiple-input multiple-output (MIMO) systems because it can balance the energy consumption and spectral efficiency (SE). However, it is challenging to obtain the optimal hybrid precoding scheme by joint optimization with lower computational complexity. This letter proposes a hybrid precoding scheme based on model-driven deep learning and alternate minimization (MDL-AltMin), which is implemented by alternately solving analog precoder and digital precoder. During the alternation, we design an analog precoding network (AP-Net) to solve the phase shift network in analog precoder with the goal of maximizing SE. The digital precoder is solved by the Lagrange multiplier method. In each alternate optimization process, the criteria for convergence is to minimize the error between the hybrid precoder and the optimal fully digital precoder. The simulation results show that the SE of our proposed scheme is very close to the fully digital precoding scheme based on singular value decomposition with lower computational complexity. |
| Author | Luo, Jie Zhang, Jinbo Fan, Jiancun |
| Author_xml | – sequence: 1 givenname: Jie orcidid: 0000-0002-8520-6125 surname: Luo fullname: Luo, Jie email: fanjc0114@gmail.com organization: School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China – sequence: 2 givenname: Jiancun orcidid: 0000-0002-7054-4324 surname: Fan fullname: Fan, Jiancun email: fanjc0114@gmail.com organization: School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China – sequence: 3 givenname: Jinbo surname: Zhang fullname: Zhang, Jinbo organization: Science and Technology on Communication Networks Laboratory, Shijiazhuang, China |
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| SubjectTerms | alternate minimization Complexity Deep learning Energy consumption Hybrid precoding Lagrange multiplier Millimeter waves Minimization mmWave Optimization Optimized production technology Precoding Radio frequency Singular value decomposition Training |
| Title | MDL-AltMin: A Hybrid Precoding Scheme for mmWave Systems With Deep Learning and Alternate Optimization |
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