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

Minsig Han; Ameha T. Abebe; Chung G. Kang

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arXiv · 2305.07945

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

Minsig Han·Ameha T. Abebe·Chung G. Kang

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Abstract

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.

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BibTeX RIS

Minsig Han, Ameha T. Abebe, Chung G. Kang. 2023-05-19. Deep Learning-based Data-aided Activity Detection with Extraction Network in Grant-free Sparse Code Multiple Access Systems. https://arxiv.org/abs/2305.07945

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