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This letter proposes a deep learning-based data-aided active user detectionnetwork (D-AUDN) for grant-free sparse code multiple access (SCMA) systems thatleverages 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 areassociated with each codebook. To address this, a user activity extractionnetwork (UAEN) is integrated within the D-AUDN to extract a-priori activityinformation from the codebook, improving activity detection of the associatedpreambles. Additionally, efficient SCMA codebook design and Zadoff-Chu preambleassociation are considered to further enhance performance.

Deep Learning-based Data-aided Activity Detection with Extraction Network in Grant-free Sparse Code Multiple Access Systems