This is the Tensorflow 2.x implementation of our paper "Deep Learning-Based Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems", IEEE Access, vol. 10, pp. 72348-72362, 2022.

Hybrid analog-digital signal processing (HSP) is an enabling technology to harvest the potential of millimeter-wave (mmWave) massive-MIMO communications. In this paper, we present a general deep learning (DL) framework for efficient design and implementation of HSP-based massive-MIMO systems. Exploiting the fact that any complex matrix can be written as a scaled sum of two matrices with unit-modulus entries, a novel analog deep neural network (ADNN) structure is first developed which can be implemented with common radio frequency (RF) components. This structure is then embedded into an extended hybrid analog-digital deep neural network (HDNN) architecture which facilitates the implementation of mmWave massive-MIMO systems while improving their performance. In particular, the proposed HDNN architecture enables HSP-based massive-MIMO transceivers to approximate any desired transmitter and receiver mapping with arbitrary precision. To demonstrate the capabilities of the proposed DL framework, we present a new HDNN-based beamformer design that can achieve the same performance as fully-digital beamforming, with reduced number of RF chains. Finally, simulation results are presented confirming the advantages of the proposed HDNN design over existing hybrid beamforming schemes.

If you find our code useful for your research, please consider citing:

@article{morsali2022deep,
  title={Deep Learning-Based Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems},
  author={Morsali, Alireza and Haghighat, Afshin and Champagne, Benoit},
  journal={IEEE Access},
  volume={10},
  pages={72348--72362},
  year={2022},
  publisher={IEEE}
}