Deep Learning-Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction

@article{Han2020DeepLF,
  title={Deep Learning-Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction},
  author={Y. Han and Mengyuan Li and S. Jin and Chao-Kai Wen and Xiaoli Ma},
  journal={IEEE Journal on Selected Areas in Communications},
  year={2020},
  volume={38},
  pages={1980-1993}
}
  • Y. Han, Mengyuan Li, +2 authors Xiaoli Ma
  • Published 2020
  • Computer Science, Mathematics
  • IEEE Journal on Selected Areas in Communications
This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel… Expand
2 Citations

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