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.
翻译:在不给赠款的稀有代码多存(GF-SCMA)系统中,主动用户检测(AUD)是一个主要的性能瓶颈,因为它涉及复杂的组合问题,使得联合设计用户和接受者AUD的争议资源成为关键但具有挑战性的问题。为此,我们提议在编码器侧和数据辅助的AUD两个序言生成网络(PGNs)进行基于自动编码的优化,在解码器侧和数据辅助的AUD(解码器侧)中,积极用户检测(AUD)是一个主要的性能瓶颈,因为它涉及复杂的组合问题,使得为数据辅助AUDUD(AUD)的SCMA编码数据数据数据联合设计了一个前期用户活动信息。拟议AE的端对端培训有助于联合优化争议资源,即序言序列,以及从序言和基于解码器的ASF$10数据传输中提取用户活动信息。此外,我们提议在数据解码器解码器解码中,为UAEN(UAEN)在数据端至D(A-B)网络内部的深度检测率显示A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A