  Massive machine-type communications (mMTC) in 6G requires supporting a
massive number of devices with limited resources, posing challenges in
efficient random access. Grant-free random access and uplink non-orthogonal
multiple access (NOMA) are introduced to increase the overload factor and
reduce transmission latency with signaling overhead in mMTC. Sparse code
multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as
advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices
(MTD) transmit information to the base station (BS) without a grant, creating a
challenging task for the BS to identify the active MTD among all potential
active devices. In this paper, a novel pre-activated residual neural
network-based multi-user detection (MUD) scheme for the grant-free SCMA and
MUSA system in an mMTC uplink framework is proposed to jointly identify the
number of active MTDs and their respective messages in the received signal's
sparsity and the active MTDs in the absence of channel state information. A
novel residual unit designed to learn the properties of multi-dimensional SCMA
codebooks, MUSA spreading sequences, and corresponding combinations of active
devices with diverse settings. The proposed scheme learns from the labeled
dataset of the received signal and identifies the active MTDs from the received
signal without any prior knowledge of the device sparsity level. A calibration
curve is evaluated to verify the model's calibration. The application of the
proposed MUD scheme is investigated in an indoor factory setting using four
different mmWave channel models. Numerical results show that when the number of
active MTDs in the system is large, the proposed MUD has a significantly higher
probability of detection compared to existing approaches over the
signal-to-noise ratio range of interest.
