  Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO)
is a promising technique for the next generation communication systems.
However, the hardware cost and power consumption grow significantly as the
number of radio frequency (RF) components increases, which hampers the
deployment of practical massive MIMO systems. To address this issue and further
facilitate the commercialization of massive MIMO, mixed analog-to-digital
converters (ADCs) architecture has been considered, where parts of
conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In
this paper, we first propose a deep learning-based (DL) joint pilot design and
channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we
devise a pilot design neural network whose weights directly represent the
optimized pilots, and develop a Runge-Kutta model-driven densely connected
network as the channel estimator. Instead of randomly assigning the mixed-ADCs,
we then design a novel antenna selection network for mixed-ADCs allocation to
further improve the channel estimation accuracy. Moreover, we adopt an
autoencoder-inspired end-to-end architecture to jointly optimize the pilot
design, channel estimation and mixed-ADCs allocation networks. Simulation
results show that the proposed DL-based methods have advantages over the
traditional channel estimators as well as the state-of-the-art networks.
