  In grant-free sparse code multiple access (GF-SCMA) system, active user
detection (AUD) is a major performance bottleneck as it involves complex
combinatorial problem, which makes joint design of contention resources for
users and AUD at the receiver a crucial but a challenging problem. To this end,
we propose autoencoder (AE)-based joint optimization of both preamble
generation networks (PGNs) in the encoder side and data-aided AUD in the
decoder side. The core architecture of the proposed AE is a novel user activity
extraction network (UAEN) in the decoder that extracts a priori user activity
information from the SCMA codeword data for the data-aided AUD. An end-to-end
training of the proposed AE enables joint optimization of the contention
resources, i.e., preamble sequences, each associated with one of the codebooks,
and extraction of user activity information from both preamble and SCMA-based
data transmission. Furthermore, we propose a self-supervised pre-training
scheme for the UAEN prior to the end-to-end training, to ensure the convergence
of the UAEN which lies deep inside the AE network. Simulation results
demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target
activity detection error rate of $\bf{{10}^{-3}}$ compared to the
state-of-the-art DL-based AUD schemes.
