论文标题
使用用户活动提取网络的数据辅助主动用户检测，用于拨款SCMA系统
Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems
论文作者
论文摘要
在无拨款稀疏代码多访问（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，这使得用户的争夺资源和接收器的AUD成为一个至关重要但具有挑战性的问题。为此，我们建议对编码器侧的两个序列生成网络（PGN）和解码器端的数据辅助AUD进行自动编码器（AE）的关节优化。提出的AE的核心体系结构是解码器中新型的用户活动提取网络（UAEN），它从SCMA CodeWord数据中提取先验用户活动信息，以获取数据辅助AUD。对拟议的AE进行的端到端培训可以使争论资源的联合优化，即序言序列，每种序列，每个序列与其中一份代码书关联，以及从序言和基于SCMA的数据传输中提取用户活动信息。此外，我们在端到端培训之前为UAEN提出了一个自制的预训练计划，以确保AE网络内部深处的UAEN的收敛性。仿真结果表明，与基于最先进的DL DL AUD方案相比，提出的AUD方案以目标活动检测错误率为$ \ bf {{10}^{ - 3}} $以3至5DB的增益获得。
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.
