# Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments

arXiv: https://arxiv.org/abs/2503.10280 · HTML: https://mlsanity.com/paper/2503.10280/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [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)

## Abstract (arXiv)

Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. This sparse support recovery of device activity status is performed by a finite cluster of access points (APs) from a large number of geographically distributed APs collaborating to serve a larger number of devices. Active devices transmit non-orthogonal pilot sequences to APs, which forward the received signals to a central processing unit (CPU) for collaborative activity detection. This paper proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU. Furthermore, the study assesses the algorithm's robustness to input perturbations and examines the effects of adopting fixed-point representation on its performance.