Association-Aware GNN for Precoder Learning in Cell-Free Systems
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 complexi...
| Authors | Mingyu Deng, Shengqian Han |
|---|---|
| Primary category | eess.SP |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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