# Deep Learning-Based Active User Detection for Grant-free SCMA Systems

arXiv: https://arxiv.org/abs/2106.11198 · HTML: https://mlsanity.com/paper/2106.11198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [6G White Paper on Machine Learning in Wireless Communication Networks](https://mlsanity.com/paper/2004.13875/) (2020)
- [Grant-free Non-orthogonal Multiple Access for IoT: A Survey](https://mlsanity.com/paper/1910.06529/) (2019)
- [White Paper on Broadband Connectivity in 6G](https://mlsanity.com/paper/2004.14247/) (2020)
- [Deep Neural Network Based Active User Detection for Grant-free NOMA Systems](https://mlsanity.com/paper/1912.11782/) (2019)

## Built on by (most cited first, 1 of 1)

- [Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems](https://mlsanity.com/paper/2205.10780/) (2022)

## Abstract (arXiv)

Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.