Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming

@article{Hojatian2022FlexibleUL,
  title={Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming},
  author={Hamed Hojatian and J{\'e}r{\'e}my Nadal and Jean-François Frigon and Franccois Leduc-Primeau},
  journal={GLOBECOM 2022 - 2022 IEEE Global Communications Conference},
  year={2022},
  pages={3833-3838}
}
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search… 
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