  In this article, deep learning is applied to estimate the uplink channels for
mixed analog-to-digital converters (ADCs) massive multiple-input
multiple-output (MIMO) systems, where a portion of antennas are equipped with
high-resolution ADCs while others employ low-resolution ones at the base
station. A direct-input deep neural network (DI-DNN) is first proposed to
estimate channels by using the received signals of all antennas. To eliminate
the adverse impact of the coarsely quantized signals, a selective-input
prediction DNN (SIP-DNN) is developed, where only the signals received by the
high-resolution ADC antennas are exploited to predict the channels of other
antennas as well as to estimate their own channels. Numerical results show the
superiority of the proposed DNN based approaches over the existing methods,
especially with mixed one-bit ADCs, and the effectiveness of the proposed
approaches on different ADC resolution patterns.
