# Deep Neural Network Based Active User Detection for Grant-free NOMA Systems

arXiv: https://arxiv.org/abs/1912.11782 · HTML: https://mlsanity.com/paper/1912.11782/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Machine Learning for Massive Industrial Internet of Things](https://mlsanity.com/paper/2103.08308/) (2021)
- [Deep Learning-Based Active User Detection for Grant-free SCMA Systems](https://mlsanity.com/paper/2106.11198/) (2021)
- [Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems](https://mlsanity.com/paper/2205.10780/) (2022)
- [Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network](https://mlsanity.com/paper/2209.06392/) (2022)
- [Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication](https://mlsanity.com/paper/2403.07255/) (2024)
- [Fast MLE and MAPE-Based Device Activity Detection for Grant-Free Access via PSCA and PSCA-Net](https://mlsanity.com/paper/2503.15259/) (2025)

## Abstract (arXiv)

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 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 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 applying 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 in both AUD success probability and computational complexity.