# An Algorithm for Grant-Free Random Access in Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/2105.10378 · HTML: https://mlsanity.com/paper/2105.10378/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2021.

## Built on by (most cited first, 3 of 3)

- [Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2406.07160/) (2024)
- [Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments](https://mlsanity.com/paper/2503.10280/) (2025)
- [Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2503.11305/) (2025)

## Abstract (arXiv)

Massive access is one of the main use cases of beyond 5G (B5G) wireless networks and massive MIMO is a key technology for supporting it. Prior works studied massive access in the co-located massive MIMO framework. In this paper, we investigate the activity detection in grant-free random access for massive machine type communications (mMTC) in cell-free massive MIMO network. Each active device transmits a pre-assigned non-orthogonal pilot sequence to the APs and the APs send the received signals to a central processing unit (CPU) for joint activity detection. We formulate the maximum likelihood device activity detection problem and provide an algorithm based on coordinate descent method having affordable complexity. We show that the cell-free massive MIMO network can support low-powered mMTC devices and provide a broad coverage.