Corpus ID: 226281557

Learning to Beamform in Heterogeneous Massive MIMO Networks

@article{Zhu2020LearningTB,
  title={Learning to Beamform in Heterogeneous Massive MIMO Networks},
  author={Minghe Zhu and Tsung-Hui Chang and Mingyi Hong},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.03971}
}
It is well-known that the problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks is challenging because of its non-convexity, and conventional optimization based algorithms suffer from high computational costs. While computationally efficient deep learning based methods have been proposed, their complexity heavily relies upon system parameters such as the number of transmit antennas, and therefore these methods typically do not generalize well when… Expand
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