  This paper is concerned with channel estimation in MIMO systems with few-bit
ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel
estimator obtained in closed-form is not an optimal solution. We first consider
a deep neural network (DNN) and train it as a non-linear MMSE channel estimator
for few-bit MIMO systems. We then present a first attempt to use DNN in
optimizing the training signal and the MMSE channel estimator concurrently.
Specifically, we propose an autoencoder with a specialized first layer, whose
weights embed the training signal matrix. Consequently, the trained autoencoder
prompts a new training signal design that is customized for the MIMO channel
model under consideration.
