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.

翻译：本文提出一种基于深度学习的辅助数据活动用户检测网络（D-AUDN），用于免授权稀疏码多址接入（SCMA）系统，该网络同时利用SCMA码本和Zadoff-Chu前导码进行活动检测。由于数据与前导码分布存在差异以及码本冲突，当多个前导码与同一码本关联时，现有D-AUDN会面临性能下降问题。为解决此问题，我们在D-AUDN中集成用户活动提取网络（UAEN），从码本中提取先验活动信息，从而提升关联前导码的活动检测性能。此外，还考虑了高效的SCMA码本设计与Zadoff-Chu前导码关联方案以进一步增强性能。