# A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems

arXiv: https://arxiv.org/abs/2410.02775 · HTML: https://mlsanity.com/paper/2410.02775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Foundations of User-Centric Cell-Free Massive MIMO](https://mlsanity.com/paper/2108.02541/) (2021)
- [User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions](https://mlsanity.com/paper/2104.14589/) (2021)
- [User-Centric 5G Cellular Networks: Resource Allocation and Comparison with the Cell-Free Massive MIMO Approach](https://mlsanity.com/paper/1803.02261/) (2018)
- [Scalability Aspects of Cell-Free Massive MIMO](https://mlsanity.com/paper/1902.11275/) (2019)
- [Intent Classification in Question-Answering Using LSTM Architectures](https://mlsanity.com/paper/2001.09330/) (2020)

## Built on by (most cited first, 2 of 2)

- [Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks](https://mlsanity.com/paper/2512.17466/) (2025)
- [A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks](https://mlsanity.com/paper/2503.04278/) (2025)

## Abstract (arXiv)

Contrary to conventional massive MIMO cellular configurations plagued by inter-cell interference, cell-free massive MIMO systems distribute network resources across the coverage area, enabling users to connect with multiple access points (APs) and boosting both system capacity and fairness across user. In such systems, one critical functionality is the association between APs and users: determining the optimal association is indeed a combinatorial problem of prohibitive complexity. In this paper, a solution based on deep learning is thus proposed to solve the user clustering problem aimed at maximizing the sum spectral efficiency while controlling the number of active connections. The proposed solution can scale effectively with the number of users, leveraging long short-term memory cells to operate without the need for retraining. Numerical results show the effectiveness of the proposed solution, even in the presence of imperfect channel state information due to pilot contamination.