Active User Detection of Machine-type Communications via Dimension Spreading Neural Network

Abstract

Massive machine-type communication (mMTC), key component for internet of things (IoT), concerns the access of massive machine-type communication devices to the basestation. To support the massive connectivity, grant-free access and non-orthogonal multiple access (NOMA) have been recently introduced. In the grant-free transmission, each device transmits information without the granting process so that the basestation 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 find out the active devices from the superimposed received signal. An aim of this paper is to propose a new type of AUD scheme suitable for the highly overloaded mMTC, referred to as dimension spreading deep neural network-based AUD (DSDNNAUD). The key feature of DSDNN-AUD is to set the dimension of hidden layers being larger than the size of a transmit vector to improve the representation quality of the support. In doing so, the proposed scheme can better discriminate the supports generated from correlated structured environment. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and throughput performance.