Large-scale deployment of massive device connectivity is a crucial communication challenge for Internet of Things (IoT) networks, which consist of a huge number of devices with sporadic traffic. In massive Machine Communication Scenario (mMTC), it is very important for the serving base-station (BS) to identify the active devices in each coherence block. This paper proposes a deep neural network (DNN) based on variational autoencoder (VAE) for device activity detection in mMTC under imperfect channel state information (CSI). A framework of variational optimization is constructed and the learning network structure is also designed. The derivation on the loss function for network training is presented and numerical results are provided to illustrate the accuracy of our method. The performance demonstrates the merits of the proposed method by comparison with the traditional compressed sensing algorithms, which are widely applied in multi-user detection.

中文翻译：

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基于变分自编码器的不完善CSI下mMTC设备活动检测深度学习方法

大规模设备连接的大规模部署是物联网 (IoT) 网络的关键通信挑战，该网络由大量具有零星流量的设备组成。在大规模机器通信场景 (mMTC) 中，服务基站 (BS) 识别每个一致性块中的活动设备非常重要。本文提出了一种基于变分自编码器 (VAE) 的深度神经网络 (DNN)，用于 mMTC 中不完善信道状态信息 (CSI) 下的设备活动检测。构建了变分优化框架，设计了学习网络结构。介绍了网络训练损失函数的推导，并提供了数值结果来说明我们方法的准确性。