Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.
翻译:多用户共享存取(MUSA)是作为高级代码域域非垂直复合传播序列引入的,用于支持大量机型通信设备;在本文件中,我们提议为无赠款MUSA系统建立一个新型的深神经网络(DNN)多用户探测系统;基于DNNMU的MUD模型决定了在模型培训阶段,由几个隐藏节点、神经激活装置和一个合适的丢失功能来决定感测矩阵的结构、随机分布的噪音和装置间干扰。透彻学习的DNNN模型能够在不事先了解设备宽度水平和频道状态信息的情况下区分接收信号的主动装置。我们的数字评估显示,与常规方法相比,DNNM-MUD的探测概率大为增加。