  The central challenge in massive machine-type communications (mMTC) is to
connect a large number of uncoordinated devices through a limited spectrum. The
typical mMTC communication pattern is sporadic, with short packets. This could
be exploited in grant-free random access in which the activity detection,
channel estimation, and data recovery are formulated as a sparse recovery
problem and solved via compressed sensing algorithms. This approach results in
new challenges in terms of high computational complexity and latency. We
present how data-driven methods can be applied in grant-free random access and
demonstrate the performance gains. Variations of neural networks for the
problem are discussed, as well as future challenges and potential directions.
