  In massive machine-type communications, data transmission is usually
considered sporadic, and thus inherently has a sparse structure. This paper
focuses on the joint activity detection (AD) and channel estimation (CE)
problems in massive-connected communication systems with low-resolution
analog-to-digital converters. To further exploit the sparse structure in
transmission, we propose a maximum posterior probability (MAP) estimation
problem based on both sporadic activity and sparse channels for joint AD and
CE. Moreover, a majorization-minimization-based method is proposed for solving
the MAP problem. Finally, various numerical experiments verify that the
proposed scheme outperforms state-of-the-art methods.
