  Estimation in few-bit MIMO systems is challenging, since the received signals
are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose
a deep learning framework for channel estimation, data detection, and pilot
signal design to address the nonlinearity in such systems. The proposed channel
estimation and data detection networks are model-driven and have special
structures that take advantage of the domain knowledge in the few-bit
quantization process. While the first data detection network, namely B-DetNet,
is based on a linearized model obtained from the Bussgang decomposition, the
channel estimation network and the second data detection network, namely
FBM-CENet and FBM-DetNet respectively, rely on the original quantized system
model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel
estimation and data detection problems are reformulated to overcome the
vanishing gradient issue. An important feature of the proposed FBM-CENet
structure is that the pilot matrix is integrated into its weight matrices of
the channel estimator. Thus, training the proposed FBM-CENet enables a joint
optimization of both the channel estimator at the base station and the pilot
signal transmitted from the users. Simulation results show significant
performance gain in estimation accuracy by the proposed deep learning
framework.
