User activity detection for massive Internet of things with an improved residual convolutional neural network,Transactions on Emerging Telecommunications Technologies

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User activity detection for massive Internet of things with an improved residual convolutional neural network
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-12-16 , DOI: 10.1002/ett.4182
Xiaojiang Wu ₁ , Guobing Li ₁ , Guomei Zhang ₁

Affiliation

Massive user activity detection is a challenging task for massive Internet of things (mIoT). In this paper, we propose a new deep neural network, named concentrated layers convolutional neural network (CLCNN), for user activity detection in mIoT. We firstly propose three basic rules in the design of residual network specifically for mIoT scenarios. Secondly, with the rules above we develop a new improved residual network block which includes integrated convolutional layers with activation functions, by which the residual convolutional network is constructed. Moreover, the regularization and its corresponding hyperparameter for the proposed network are also investigated against overfitting. Simulation results show that the proposed CLCNN network outperforms the existing deep learning algorithm and conventional compressive sensing solutions in user activity detection and corresponding channel estimation.

中文翻译：

改进的残积卷积神经网络用于大规模物联网的用户活动检测

对于大规模物联网（mIoT），大规模用户活动检测是一项具有挑战性的任务。在本文中，我们提出了一种新的深度神经网络，称为集中层卷积神经网络（CLCNN），用于mIoT中的用户活动检测。我们首先在针对mIoT场景的残差网络设计中提出了三个基本规则。其次，根据上述规则，我们开发了一种新的改进的残差网络块，其中包括具有激活函数的集成卷积层，从而构造了残差卷积网络。此外，还针对超拟合研究了拟议网络的正则化及其相应的超参数。

更新日期：2021-02-10

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