As a means to support the access of massive machine-type communication devices, grant-free access and non-orthogonal multiple access (NOMA) have received great deal of attention in recent years. In the grant-free transmission, each device transmits information without the granting process so that the base station needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to identify active devices from the superimposed received signal. An aim of this paper is to put forth a new type of AUD based on deep neural network (DNN). By feeding the training data in the properly designed DNN, the proposed AUD scheme learns the nonlinear mapping between the received NOMA signal and indices of active devices. As a result, the trained DNN can handle the whole AUD process, achieving an accurate detection of the active users. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches by a large margin in both AUD success probability and computational complexity.

中文翻译：

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用于免授权 NOMA 系统的基于深度神经网络的主动用户检测

作为支持海量机器类通信设备接入的手段，免授权接入和非正交多址接入（NOMA）近年来受到了广泛关注。在免授权传输中，每个设备在没有授权过程的情况下传输信息，使得基站需要在所有潜在设备中识别活动设备。这个过程称为活动用户检测 (AUD)，在基于 NOMA 的系统中是一个具有挑战性的问题，因为很难从叠加的接收信号中识别活动设备。本文的一个目的是提出一种基于深度神经网络（DNN）的新型AUD。通过在适当设计的 DNN 中提供训练数据，所提出的 AUD 方案学习接收到的 NOMA 信号与有源设备索引之间的非线性映射。因此，经过训练的 DNN 可以处理整个 AUD 过程，实现对活跃用户的准确检测。数值结果表明，所提出的 AUD 方案在 AUD 成功概率和计算复杂性方面都大大优于传统方法。