A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

@article{Elbir2019AFO,
  title={A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO},
  author={Ahmet M. Elbir and Kumar Vijay Mishra and M. R. Bhavani Shankar and Bj{\"o}rn E. Ottersten},
  journal={IEEE Transactions on Cognitive Communications and Networking},
  year={2019},
  volume={8},
  pages={642-656}
}
  • A. ElbirK. Mishra B. Ottersten
  • Published 20 December 2019
  • Computer Science
  • IEEE Transactions on Cognitive Communications and Networking
Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels… 
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