Massive machine-type communications (mMTC) in 6G requires supporting a massive number of devices with limited resources, posing challenges in efficient random access. Grant-free random access and uplink non-orthogonal multiple access (NOMA) are introduced to increase the overload factor and reduce transmission latency with signaling overhead in mMTC. Sparse code multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices (MTD) transmit information to the base station (BS) without a grant, creating a challenging task for the BS to identify the active MTD among all potential active devices. In this paper, a novel pre-activated residual neural network-based multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an mMTC uplink framework is proposed to jointly identify the number of active MTDs and their respective messages in the received signal's sparsity and the active MTDs in the absence of channel state information. A novel residual unit designed to learn the properties of multi-dimensional SCMA codebooks, MUSA spreading sequences, and corresponding combinations of active devices with diverse settings. The proposed scheme learns from the labeled dataset of the received signal and identifies the active MTDs from the received signal without any prior knowledge of the device sparsity level. A calibration curve is evaluated to verify the model's calibration. The application of the proposed MUD scheme is investigated in an indoor factory setting using four different mmWave channel models. Numerical results show that when the number of active MTDs in the system is large, the proposed MUD has a significantly higher probability of detection compared to existing approaches over the signal-to-noise ratio range of interest.

翻译：大规模机器类通信（mMTC）作为6G的关键场景，需要以有限资源支持海量设备接入，这对高效随机接入机制提出了挑战。为提升过载因子、降低传输时延并减少信令开销，免授权随机接入与上行非正交多址接入（NOMA）技术被引入mMTC系统。其中，稀疏码多址接入（SCMA）与多用户共享接入（MUSA）作为先进的码域NOMA方案，受到广泛关注。在免授权NOMA场景中，机器类设备（MTD）无需授权即可向基站（BS）传输信息，这要求BS从所有潜在激活设备中准确识别当前活跃的MTD。本文针对mMTC上行链路框架下的免授权SCMA与MUSA系统，提出一种基于预激活残差神经网络的多用户检测（MUD）方案，可在无信道状态信息条件下，联合估计接收信号稀疏度、识别活跃MTD数量及其对应消息。该方案设计了新型残差单元，用于学习多维SCMA码本、MUSA扩频序列以及不同设备激活组合的关联特征。所提方法从接收信号的标注数据集中学习，无需设备稀疏度先验知识即可完成活跃MTD识别。通过校准曲线评估模型校准性能，并在四种不同毫米波信道模型下验证所提MUD方案在室内工厂场景中的应用效果。数值结果表明，当系统中活跃MTD数量较大时，所提MUD方案在关注信噪比范围内的检测概率显著优于现有方法。