Unsupervised mmWave Beamforming via Autoencoders

Ture Peken, Ravi Tandon, Tamal Bose

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

5 Scopus citations


We provide unsupervised machine learning (ML) schemes based on autoencoders for unconstrained beamforming (BF) and hybrid BF in millimeter-waves (mmWaves). An autoencoder is a powerful unsupervised ML model, and it is used to reconstruct the input with a minimal error by finding a low-dimensional representation of the input. In this paper, we present a linear autoencoder for finding the beamformers at the transmitter (Tx) and receiver (Rx), which maximize the achieved rates over the mmWave channel. Since the autoencoder has a close relationship with the singular value decomposition (SVD), we first study autoencoders for unconstrained BF based on SVD. In hybrid BF, beamformers are designed by using finite-precision phase shifters in the radio frequency (RF) domain along with power constraints. Therefore, we propose a hybrid BF algorithm based on autoencoders, which incorporates these constraints. We present our simulation results for both unconstrained BF as well as hybrid BF, and compare their performance with state-of-the-art. By using the stochastic and NYUSIM channel models, we achieve 30 - 40% and 60 - 70% gains in rates with the proposed autoencoder based approach compared to the supervised hybrid BF with the stochastic and NYUSIM channel models, respectively.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150895
StatePublished - Jun 2020
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: Jun 7 2020Jun 11 2020

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2020 IEEE International Conference on Communications, ICC 2020

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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