# Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks

arXiv: https://arxiv.org/abs/2408.02359 · HTML: https://mlsanity.com/paper/2408.02359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2503.11305/) (2025)

## Abstract (arXiv)

Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO networks is proposed. Initially, the known non-orthogonal pilot sequences are used to estimate the channel coefficients between each user and the access points. Then, a deep convolutional neural network is used to estimate the activity status of the users. The proposed method is "blind", i.e., it is fully data-driven and does not require prior large-scale fading coefficients estimation. Numerical results show how the proposed DL-based algorithm is able to merge the information gathered by the distributed antennas to estimate the user activity status, yet outperforming a state-of-the-art covariance-based method.