Deep Learning Based Decentralized Beamforming Methods for Multi-Antenna Interference Channels

Minseok Kim, Hoon Lee, Mintae Kim, Inkyu Lee

Research output: Contribution to journalArticlepeer-review


This paper develops deep learning (DL) based beamforming approaches for multi-antenna interference channels where several base stations (BSs) individually optimize their own beamforming vectors in a decentralized manner. By exploiting the optimal beam structure, we propose an efficient method for beam decisions and coordination among BSs based solely on local information. Moreover, we show that the proposed approach allows a scalable design with respect to the number of users. We also present novel training strategies for the proposed deep neural networks, validating its potential as an innovative decentralized beamforming methodology. Consequently, the proposed DL based decentralized beamforming framework can achieve various optimal beamforming strategies. Numerical results demonstrate the advantages of the proposed framework over conventional methods.

Original languageEnglish
Pages (from-to)140853-140866
Number of pages14
JournalIEEE Access
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Deep learning
  • decentralized beamforming
  • interference channel

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


Dive into the research topics of 'Deep Learning Based Decentralized Beamforming Methods for Multi-Antenna Interference Channels'. Together they form a unique fingerprint.

Cite this