  Channel estimation is a challenging task, especially in a massive
multiple-input multiple-output (MIMO) system with one-bit analog-to-digital
converters (ADC). Traditional deep learning (DL) methods, that learn the
mapping from inputs to real channels, have significant difficulties in
estimating accurate channels because their loss functions are not well designed
and investigated. In this paper, a conditional generative adversarial networks
(cGAN) is developed to predict more realistic channels by adversarially
training two DL networks. cGANs not only learn the mapping from quantized
observations to real channels but also learn an adaptive loss function to
correctly train the networks. Numerical results show that the proposed cGAN
based approach outperforms existing DL methods and achieves high robustness in
massive MIMO systems.
