# A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2503.04278 · HTML: https://mlsanity.com/paper/2503.04278/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Ubiquitous Cell-Free Massive MIMO Communications](https://mlsanity.com/paper/1804.03421/) (2018)
- [User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions](https://mlsanity.com/paper/2104.14589/) (2021)
- [A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead](https://mlsanity.com/paper/1604.00786/) (2016)
- [Multi-Objective Signal Processing Optimization: The Way to Balance Conflicting Metrics in 5G Systems](https://mlsanity.com/paper/1406.2871/) (2014)
- [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)
- [Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2109.03128/) (2021)
- [Path Planning Using Probability Tensor Flows](https://mlsanity.com/paper/2003.02774/) (2020)
- [Co-existing/Cooperating Multicell Massive MIMO and Cell-Free Massive MIMO Deployments: Heuristic Designs and Performance Analysis](https://mlsanity.com/paper/2404.15530/) (2024)
- [A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2410.02775/) (2024)
- [Approaching Massive MIMO Performance with Reconfigurable Intelligent Surfaces: We Do Not Need Many Antennas](https://mlsanity.com/paper/2203.07493/) (2022)
- [Hybrid Network- and User-Centric Scalable Cell-Free Massive MIMO for Fronthaul Signaling Minimization](https://mlsanity.com/paper/2412.15475/) (2024)

## Abstract (arXiv)

This study addresses the challenge of access point (AP) and user equipment (UE) association in cell-free massive MIMO networks. It introduces a deep learning algorithm leveraging Bidirectional Long Short-Term Memory cells and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of UEs without requiring retraining. Additionally, the study presents a training methodology that improves scalability not only with respect to the number of UEs but also to the number of APs. Furthermore, a variant of the proposed AP-UE algorithm ensures robustness against pilot contamination effects, a critical issue arising from pilot reuse in channel estimation. Extensive numerical results validate the effectiveness and adaptability of the proposed methods, demonstrating their superiority over widely used heuristic alternatives.