Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Jienan Chen, Wei Feng, Jing Xing, Ping Yang, Gerald E. Sobelman, Dengsheng Lin, Shaoqian Li

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Hybrid analog/digital processing is crucial for millimeter-wave (mmWave) MIMO systems due to its ability to balance the gain and cost. Despite fruitful recent studies, the optimal beamforming/combining method remains unknown for a practical multiuser, broadband mmWave MIMO equipped with low-resolution phase shifters and low-resolution analog-to-digital converters (ADCs). In this paper, we leverage artificial intelligence techniques to tackle this problem. Particularly, we propose a neural hybrid beamforming/combining (NHB) MIMO system, where the various types of hybrid analog/digital mmWave MIMO systems are transformed into a corresponding autoencoder (AE) based neural networks. Consequently, the digital and analog beamforming/combiners are obtained by training the AE based new model in an unsupervised learning manner, regardless of particular configurations. Using this approach, we can apply a machine learning-based design methodology that is compatible with a range of different beamforming/combing architectures. We also propose an iterative training strategy for the neural network parameter updating, which can effectively guarantee fast convergence of the established NHB model. According to simulation results, the proposed NHB can offer a significant performance gain over existing methods in terms of bit error rate (BER). Moreover, NHB can fast formulate the neural network parameters as channel changed, which is believed more promising for practice due to its better flexibility and compatibility.

Original languageEnglish (US)
Article number9144455
Pages (from-to)11353-11368
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Issue number10
StatePublished - Oct 2020

Bibliographical note

Funding Information:
Manuscript received January 5, 2020; revised April 17, 2020 and June 9, 2020; accepted July 12, 2020. Date of publication July 20, 2020; date of current version October 22, 2020. This work was supported in part by the National Natural Science Foundation of China under Grants 61941104, 61876033, and 61971107 and in part by the State Key Lab. of ASIC and Systems under Grant 2018GF016. The source code is availiable at https://github. com/jingxing10/Hybrid-Beamforming-Combining-in-broadband-system. The review of this aritcle was coordinated by Dr. B. Mao. (Corresponding author: Ping Yang.) Jienan Chen, Jing Xing, Ping Yang, Dengsheng Lin, and Shaoqian Li are with the National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: jesson.chen@outlook.com; jingxing@std.uestc.edu.cn; yang.ping@uestc.edu.cn; linds@uestc.edu.cn; lsq@uestc.edu.cn).

Publisher Copyright:
© 1967-2012 IEEE.


  • Artificial intelligence
  • hybrid beamforming
  • millimeter wave
  • multiple-input and multiple-output


Dive into the research topics of 'Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach'. Together they form a unique fingerprint.

Cite this