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

2023/05/13 by Minsig Han, Han, Minsig, Ameha T. Abebe +3
Engineering · Computer Science · #Advanced Wireless Communication Technologies #Gaze Tracking and Assistive Technology #IoT and Edge/Fog Computing

paper · pdf · doi:10.48550/arxiv.2305.07945

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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