# Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks

arXiv: https://arxiv.org/abs/2503.11305 · HTML: https://mlsanity.com/paper/2503.11305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## 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)
- [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)
- [Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks](https://mlsanity.com/paper/2408.02359/) (2024)

## Abstract (arXiv)

Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks, particularly in the context of 5G and beyond (6G) systems. Within the context of GF-RA, this study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD). GF-RA addresses scalability by employing non-orthogonal pilot sequences, which provides an efficient alternative comparing to conventional grant-based random access (GB-RA) technique that are constrained by the scarcity of orthogonal preamble resources. In this paper, we propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. We propose two distinct framework deployment strategies, centralized and decentralized, both tailored to streamline the proposed approach implementation across network infrastructures. Moreover, we introduce optimized post-detection methodologies complemented by a clustering stage to enhance overall detection performances. Our 3GPP-compliant simulations have validated that the proposed algorithm achieves state-of-the-art model-based activity detection accuracy while significantly reducing complexity. Achieving 99% accuracy, it demonstrates real-world viability and effectiveness.