论文标题
关于GF-SCMA系统的基于深度学习的数据辅助主动用户检测的性能
On the Performance of Deep Learning-based Data-aided Active User Detection for GF-SCMA System
论文作者
论文摘要
在接收器中，针对发射器和数据辅助主动用户检测（AUD）基于深度学习（DL）基于深度学习（DL）的联合设计的最新作品表明，对无授予的稀疏代码多重访问（GF-SCMA）系统进行了显着改进的性能。联合设计的自动编码器只能在给定环境中进行培训，但是在操作环境不断变化的实际情况下，很难为每个可能的环境优化序言集。因此，一种常规但一般的方法可以在依靠独立设计而不是联合设计的序言集的同时实现数据辅助的AUD。在本文中，直接比较了数据辅助AUD的活动检测错误率（ADER）的性能，即受两个序言设计，即独立设计的序言和共同设计的序言，直接比较。幸运的是，发现由独立序言设计引起的数据辅助AUD的性能损失仅限于1dB。此外，与同一密码书（CB）（平均CB Intra Inter Inter-CB互相关）之间的平均互相关来解释联合设计的前序集的这种性能特征，并在与不同CBS相关的前序中的平均互相关（平均CB的平均CB Inter-CB交叉相关）之间的平均互相关来解释。
The recent works on a deep learning (DL)-based joint design of preamble set for the transmitters and data-aided active user detection (AUD) in the receiver has demonstrated a significant performance improvement for grant-free sparse code multiple access (GF-SCMA) system. The autoencoder for the joint design can be trained only in a given environment, but in an actual situation where the operating environment is constantly changing, it is difficult to optimize the preamble set for every possible environment. Therefore, a conventional, yet general approach may implement the data-aided AUD while relying on the preamble set that is designed independently rather than the joint design. In this paper, the activity detection error rate (ADER) performance of the data-aided AUD subject to the two preamble designs, i.e., independently designed preamble and jointly designed preamble, were directly compared. Fortunately, it was found that the performance loss in the data-aided AUD induced by the independent preamble design is limited to only 1dB. Furthermore, such performance characteristics of jointly designed preamble set is interpreted through average cross-correlation among the preambles associated with the same codebook (CB) (average intra-CB cross-correlation) and average cross-correlation among preambles associated with the different CBs (average inter-CB cross-correlation).
