# Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network

arXiv: https://arxiv.org/abs/2209.06392 · HTML: https://mlsanity.com/paper/2209.06392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [Grant-free Non-orthogonal Multiple Access for IoT: A Survey](https://mlsanity.com/paper/1910.06529/) (2019)
- [Distributed Compressive Sensing: A Deep Learning Approach](https://mlsanity.com/paper/1508.04924/) (2015)
- [Deep Neural Network Based Active User Detection for Grant-free NOMA Systems](https://mlsanity.com/paper/1912.11782/) (2019)
- [IoT Connectivity Technologies and Applications: A Survey](https://mlsanity.com/paper/2002.12646/) (2020)
- [Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks](https://mlsanity.com/paper/2101.02324/) (2021)
- [DeepMuD: Multi-user Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning](https://mlsanity.com/paper/2102.09196/) (2021)
- [Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access](https://mlsanity.com/paper/2106.11204/) (2021)
- [NOMA Joint Channel Estimation and Signal Detection using Rotational Invariant Codes and GMM-based Clustering](https://mlsanity.com/paper/2202.12514/) (2022)

## Abstract (arXiv)

We consider the multi-user detection (MUD) problem in uplink grant-free non-orthogonal multiple access (NOMA), where the access point has to identify the total number and correct identity of the active Internet of Things (IoT) devices and decode their transmitted data. We assume that IoT devices use complex spreading sequences and transmit information in a random-access manner following the burst-sparsity model, where some IoT devices transmit their data in multiple adjacent time slots with a high probability, while others transmit only once during a frame. Exploiting the temporal correlation, we propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the MUD problem. The BiLSTM network creates a pattern of the device activation history using forward and reverse pass LSTMs, whereas the attention mechanism provides essential context to the device activation points. By doing so, a hierarchical pathway is followed for detecting active devices in a grant-free scenario. Then, by utilising the complex spreading sequences, blind data detection for the estimated active devices is performed. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes.