# Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks

arXiv: https://arxiv.org/abs/2101.02324 · HTML: https://mlsanity.com/paper/2101.02324/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 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)

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks. In the grant-free NOMA systems with a massive number of users, user activity detection is of great importance. Existing multi-user detection (MUD) techniques rely on complicated update steps which may cause latency in signal detection. In this paper, we propose a generative neural network-based MUD (GenMUD) framework to utilize low-complexity neural networks, which are trained to reconstruct signals in a small fixed number of steps. By exploiting the uncorrelated user behaviours, we design a network architecture to achieve higher recovery accuracy with a low computational cost. Experimental results show significant performance gains in detection accuracy compared to conventional solutions under different channel conditions and user sparsity levels. We also provide a sparsity estimator through extensive experiments. Simulation results of the sparsity estimator showed high estimation accuracy, strong robustness to channel variations and neglectable impact on support detection accuracy.