Paper Info
Title
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.
Year
DOI
Venue
2019
10.1109/ICC.2019.8761407
IEEE International Conference on Communications
Field
DocType
ISSN
Noma,Computer science,Internet of Things,Computer network,Throughput,Artificial neural network,Active devices
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Wonjun Kim130126.50
Guyoung Lim200.34
Yongjun Ahn300.68
Byonghyo Shim493788.51