  This paper considers joint user identification and channel estimation (JUICE)
in grant-free access with a \emph{clustered} user activity pattern. In
particular, we address the JUICE in massive machine-type communications (mMTC)
network under correlated Rayleigh fading channels with unknown channel
covariance matrices. We formulate the JUICE problem as a maximum \emph{a
posteriori} probability (MAP) problem with properly chosen priors to
incorporate the partial knowledge of the UEs' clustered activity and the
unknown covariance matrices. We derive a computationally-efficient algorithm
based on alternating direction method of multipliers (ADMM) to solve the MAP
problem iteratively via a sequence of closed-form updates. Numerical results
highlight the significant improvements brought by the proposed approach in
terms of channel estimation and activity detection performances for clustered
user activity patterns.
