# Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/2406.07160 · HTML: https://mlsanity.com/paper/2406.07160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [Massive Machine-type Communications in 5G: Physical and MAC-layer solutions](https://mlsanity.com/paper/1606.03893/) (2016)
- [Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2111.15378/) (2021)
- [Deep Learning Based Fast Multiuser Detection for Massive Machine-Type Communication](https://mlsanity.com/paper/1807.00967/) (2018)
- [An Algorithm for Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2105.10378/) (2021)
- [Massive MIMO for Internet of Things (IoT) Connectivity](https://mlsanity.com/paper/1905.06205/) (2019)

## Abstract (arXiv)

Modern wireless networks must reliably support a wide array of connectivity demands, encompassing various user needs across diverse scenarios. Machine-Type Communication (mMTC) is pivotal in these networks, particularly given the challenges posed by massive connectivity and sporadic device activation patterns. Traditional grant-based random access (GB-RA) protocols face limitations due to constrained orthogonal preamble resources. In response, the adoption of grant-free random access (GF-RA) protocols offers a promising solution. This paper explores the application of supervised machine learning models to tackle activity detection issues in scenarios where non-orthogonal preamble design is considered. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks operating under GF-RA protocols. Additionally, this study presents a novel clustering strategy that simplifies and enhances activity detection accuracy, assesses the resilience of the algorithm to input perturbations, and investigates the effects of adopting floating-to-fixed-point conversion on algorithm performance. Simulations conducted adhere to 3GPP standards, ensuring accurate channel modeling, and employ a deep learning approach to boost the detection capabilities of mMTC GF-RA devices. The results are compelling: the algorithm achieves an exceptional 99% accuracy rate, confirming its efficacy in real-world applications.