  This letter proposes a deep learning-based data-aided active user detection
network (D-AUDN) for grant-free sparse code multiple access (SCMA) systems that
leverages both SCMA codebook and Zadoff-Chu preamble for activity detection.
Due to disparate data and preamble distribution as well as codebook collision,
existing D-AUDNs experience performance degradation when multiple preambles are
associated with each codebook. To address this, a user activity extraction
network (UAEN) is integrated within the D-AUDN to extract a-priori activity
information from the codebook, improving activity detection of the associated
preambles. Additionally, efficient SCMA codebook design and Zadoff-Chu preamble
association are considered to further enhance performance.
