[논문]Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Kim, Wonjun; Ahn, Yongjun; Shim, Byonghyo

doi:10.1109/tcomm.2020.2969184

[해외논문] Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems 원문보기

IEEE transactions on communications, v.68 no.4, 2020년, pp.2143 - 2155

Kim, Wonjun (Institute of New Media and Communications, Seoul National University, Seoul, South Korea) , Ahn, Yongjun (Institute of New Media and Communications, Seoul National University, Seoul, South Korea) , Shim, Byonghyo (Institute of New Media and Communications, Seoul National University, Seoul, South Korea)

Abstract ▼ AI-Helper

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.

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