Channel Estimation for Massive MIMO with One-Bit ADCs


Channel estimation from highly quantized measurements using deep learning

Key ideas

  • Deriving the structure of the pilot sequence under which the mapping from quantized measurements to the full-resolution channel vector is bijective
  • Designing a deep neural network that learns the highly non-linear mapping from quantized measurements to the complex-valued channels


  • Enabling massive MIMO system with low-resolution ADCs/DACs
  • Enabling fast channel estimation in low-resolution mmWave/massive MIMO systems with a few pilot symbols

More information about this research direction

Paper: Y. Zhang, M. Alrabeiah and A. Alkhateeb, “Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots,” in IEEE Wireless Communications Letters, vol. 9, no. 8, pp. 1273-1277, Aug. 2020.

Abstract: This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework, the prior channel estimation observations and deep neural networks are leveraged to learn the non-trivial mapping from quantized received measurements to channels. For that, we derive the sufficient length and structure of the pilot sequence to guarantee the existence of this mapping function. This leads to the interesting, and counter-intuitive, observation that when more base-station antennas are employed, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length. Equivalently, for the same channel estimation performance, this means that when more antennas are employed, fewer pilots are required. This observation is also analytically proved for some special channel models. Simulation results confirm our observations and show that more antennas lead to better channel estimation in terms of the normalized mean squared error and the receive signal-to-noise ratio per antenna.

author={Zhang, Yu and Alrabeiah, Muhammad and Alkhateeb, Ahmed},
journal={IEEE Wireless Communications Letters},
title={Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots},

To reproduce the results in this paper:

Simulation codes (based on DeepMIMO v1):
These simulations use the DeepMIMO scenarios:
Example: Steps to generate the results in this figure
  1. Download all the files of this GitHub repository.
  2. Create two empty folders at the same directory as the downloaded codes and name them “Networks” and “Data” respectively. As the names indicate, “Networks” will store the trained neural networks and “Data” will store the predicted channels for evaluations.
  3. Run “main.m” in MATLAB.
  4. When “main.m” finishes, execute “Fig3_Generator.m”, which will get the figure on the right.

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