# Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access

arXiv: https://arxiv.org/abs/2106.11204 · HTML: https://mlsanity.com/paper/2106.11204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2021.

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

- [Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network](https://mlsanity.com/paper/2209.06392/) (2022)

## Abstract (arXiv)

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.