# Association-Aware GNN for Precoder Learning in Cell-Free Systems

arXiv: https://arxiv.org/abs/2603.13035 · HTML: https://mlsanity.com/paper/2603.13035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Invariant and Equivariant Graph Networks](https://mlsanity.com/paper/1812.09902/) (2018)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Foundations of User-Centric Cell-Free Massive MIMO](https://mlsanity.com/paper/2108.02541/) (2021)
- [Deep Learning Methods for Universal MISO Beamforming](https://mlsanity.com/paper/2007.00841/) (2020)
- [GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks](https://mlsanity.com/paper/2311.03659/) (2023)
- [Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks](https://mlsanity.com/paper/2401.14281/) (2024)
- [Learning-Based Joint Antenna Selection and Precoding Design for Cell-Free MIMO Networks](https://mlsanity.com/paper/2404.08607/) (2024)
- [Heterogeneous Graph Neural Network for Cooperative ISAC Beamforming in Cell-Free MIMO Systems](https://mlsanity.com/paper/2410.09963/) (2024)

## Abstract (arXiv)

Deep learning has been widely recognized as a promising approach for optimizing multi-user multi-antenna precoders in traditional cellular systems. However, a critical distinction between cell-free and cellular systems lies in the flexibility of user equipment (UE)-access point (AP) associations. Consequently, the optimal precoder depends not only on channel state information but also on the dynamic UE-AP association status. In this paper, we propose an association-aware graph neural network (AAGNN) that explicitly incorporates association status into the precoding design. We leverage the permutation equivariance properties of the cell-free precoding policy to reduce the training complexity of AAGNN and employ an attention mechanism to enhance its generalization performance. Simulation results demonstrate that the proposed AAGNN outperforms baseline learning methods in both learning performance and generalization capabilities while maintaining low training and inference complexity.