  In order to reduce hardware complexity and power consumption, massive
multiple-input multiple-output (MIMO) systems employ low-resolution
analog-to-digital converters (ADCs) to acquire quantized measurements
$\boldsymbol y$. This poses new challenges to the channel estimation problem,
and the sparse prior on the channel coefficient vector $\boldsymbol x$ in the
angle domain is often used to compensate for the information lost during
quantization. By interpreting the sparse prior from a probabilistic
perspective, we can assume $\boldsymbol x$ follows certain sparse prior
distribution and recover it using approximate message passing (AMP). However,
the distribution parameters are unknown in practice and need to be estimated.
Due to the increased computational complexity in the quantization noise model,
previous works either use an approximated noise model or manually tune the
noise distribution parameters. In this paper, we treat both signals and
parameters as random variables and recover them jointly within the AMP
framework. The proposed approach leads to a much simpler parameter estimation
method, allowing us to work with the quantization noise model directly.
Experimental results show that the proposed approach achieves state-of-the-art
performance under various noise levels and does not require parameter tuning,
making it a practical and maintenance-free approach for channel estimation.
