# Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/2503.03561 · HTML: https://mlsanity.com/paper/2503.03561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Effects of padding on LSTMs and CNNs](https://mlsanity.com/paper/1903.07288/) (2019)
- [Foundations of User-Centric Cell-Free Massive MIMO](https://mlsanity.com/paper/2108.02541/) (2021)
- [Padding Module: Learning the Padding in Deep Neural Networks](https://mlsanity.com/paper/2301.04608/) (2023)
- [Closed-form max-min power control for some cellular and cell-free massive MIMO networks](https://mlsanity.com/paper/2205.00429/) (2022)
- [Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink](https://mlsanity.com/paper/2201.04440/) (2022)

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

- [Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks](https://mlsanity.com/paper/2512.17466/) (2025)
- [Tree Meets Transformer: A Hybrid Architecture for Scalable Power Allocation in Cell-Free Networks](https://mlsanity.com/paper/2512.22639/) (2025)
- [DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness](https://mlsanity.com/paper/2602.15617/) (2026)

## Abstract (arXiv)

Power allocation is an important task in wireless communication networks. Classical optimization algorithms and deep learning methods, while effective in small and static scenarios, become either computationally demanding or unsuitable for large and dynamic networks with varying user loads. This letter explores the potential of transformer-based deep learning models to address these challenges. We propose a transformer neural network to jointly predict optimal uplink and downlink power using only user and access point positions. The max-min fairness problem in cell-free massive multiple input multiple output systems is considered. Numerical results show that the trained model provides near-optimal performance and adapts to varying numbers of users and access points without retraining, additional processing, or updating its neural network architecture. This demonstrates the effectiveness of the proposed model in achieving robust and flexible power allocation for dynamic networks.