  In this paper, we investigate the joint device activity and data detection in
massive machine-type communications (mMTC) with a one-phase non-coherent
scheme, where data bits are embedded in the pilot sequences and the base
station simultaneously detects active devices and their embedded data bits
without explicit channel estimation. Due to the correlated sparsity pattern
introduced by the non-coherent transmission scheme, the traditional approximate
message passing (AMP) algorithm cannot achieve satisfactory performance.
Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet)
that enhances the detection performance by effectively exploiting the pilot
activity correlation. The DL-mAMPnet is constructed by unfolding the AMP
algorithm into a feedforward neural network, which combines the principled
mathematical model of the AMP algorithm with the powerful learning capability,
thereby benefiting from the advantages of both techniques. Trainable parameters
are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern
and the large-scale fading coefficient. Moreover, a refinement module is
designed to further advance the performance by utilizing the spatial feature
caused by the correlated sparsity pattern. Simulation results demonstrate that
the proposed DL-mAMPnet can significantly outperform traditional algorithms in
terms of the symbol error rate performance.
