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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Vydáno v:IEEE wireless communications letters Ročník 11; číslo 9; s. 1925 - 1929
Hlavní autoři: Luo, Jie, Fan, Jiancun, Zhang, Jinbo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
ISSN:2162-2337, 2162-2345
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Shrnutí: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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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2022.3188167