RSSI-Based Hybrid Beamforming Design with Deep Learning

@article{Hojatian2020RSSIBasedHB,
  title={RSSI-Based Hybrid Beamforming Design with Deep Learning},
  author={Hamed Hojatian and V. N. Ha and J{\'e}r{\'e}my Nadal and J. Frigon and François Leduc-Primeau},
  journal={ICC 2020 - 2020 IEEE International Conference on Communications (ICC)},
  year={2020},
  pages={1-6}
}
  • Hamed Hojatian, V. N. Ha, +2 authors François Leduc-Primeau
  • Published 2020
  • Computer Science, Engineering, Mathematics
  • ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
  • Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid… CONTINUE READING
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    Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
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