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Joint user identification and channel estimation (JUICE) is a main challengein grant-free massive machine-type communications (mMTC). The sparse pattern inusers' activity allows to solve the JUICE as a compressed sensing problem in amultiple measurement vector (MMV) setup. This paper addresses the JUICE underthe practical spatially correlated fading channel. We formulate the JUICE as aniterative reweighted $\ell_{2,1}$-norm optimization. We develop acomputationally efficient alternating direction method of multipliers (ADMM)approach to solve it. In particular, by leveraging the second-order statisticsof the channels, we reformulate the JUICE problem to exploit the covarianceinformation and we derive its ADMM-based solution. The simulation resultshighlight the significant improvements brought by the proposed approach interms of channel estimation and activity detection performances.

Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC