Deep learning based beamforming neural networks in downlink MISO systems

Wenchao Xia, Gan Zheng, Yongxu Zhu, Jun Zhang, Jiangzhou Wang, Athina P. Petropulu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Beamforming techniques play an important role in multi-antenna communication systems and this work focuses on the downlink power minimization problem under a set of quality of service constraints. Conventional iterative algorithms can obtain optimal solutions but at the cost of high computational delay. Fast beamforming can be achieved by leveraging the powerful deep learning techniques. In this work, we propose a beamforming neural network (BNN), based on convolutional neural networks and exploitation of expert knowledge, for the power minimization problem. Instead of estimating beamforming matrix directly, we predict key features using the BNN which takes complex channel as input. Then the beamforming matrix is recovered from the predictions according to the uplink-downlink duality. The BNN adopts the supervised learning method with a loss function based on the mean-squared error metric to update network parameters. Simulation results show the BNN can achieve satisfactory performance with low computational delay.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123738
DOIs
StatePublished - May 2019
Event2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Shanghai, China
Duration: May 20 2019May 24 2019

Publication series

Name2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019
CountryChina
CityShanghai
Period5/20/195/24/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Aerospace Engineering

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