Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding

Hongji Huang, Yiwei Song, Jie Yang, Guan Gui, Fumiyuki Adachi

Research output: Contribution to journalArticlepeer-review

347 Citations (Scopus)


Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes are that they have high-computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable of minimizing the bit error ratio and enhancing the spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.

Original languageEnglish
Article number8618345
Pages (from-to)3027-3032
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Issue number3
Publication statusPublished - 2019 Mar


  • deep learning
  • hybrid precoding
  • massive multiple-input multiple-output (MIMO)
  • Millimeter wave (mmWave)


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