  Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a
promising solution to tackle the challenges arising from next-generation
massive machine-type communications. In this paper, a fully grant-free deep
learning (DL)-based method for user activity detection in CF-mMIMO networks is
proposed. Initially, the known non-orthogonal pilot sequences are used to
estimate the channel coefficients between each user and the access points.
Then, a deep convolutional neural network is used to estimate the activity
status of the users. The proposed method is "blind", i.e., it is fully
data-driven and does not require prior large-scale fading coefficients
estimation. Numerical results show how the proposed DL-based algorithm is able
to merge the information gathered by the distributed antennas to estimate the
user activity status, yet outperforming a state-of-the-art covariance-based
method.
