Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN
<|reference_start|>Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN: We develop a graph neural network (GNN) to compute, within a time budget of 1 to 2 milliseconds required by practical systems, the optimal linear precoder (OLP) maximizing the minimal downlink user data rate for a Cell-Free Massive MIMO system - a key 6G wireless technology. The state-of-the-art method is a bisection search on second order cone programming feasibility test (B-SOCP) which is a magnitude too slow for practical systems. Our approach relies on representing OLP as a node-level prediction task on a graph. We construct a graph that accurately captures the interdependence relation between access points (APs) and user equipments (UEs), and the permutation equivariance of the Max-Min problem. Our neural network, named OLP-GNN, is trained on data obtained by B-SOCP. We tailor the OLP-GNN size, together with several artful data preprocessing and postprocessing methods to meet the runtime requirement. We show by extensive simulations that it achieves near optimal spectral efficiency in a range of scenarios with different number of APs and UEs, and for both line-of-sight and non-line-of-sight radio propagation environments.<|reference_end|>
@article{parlier2024learning,
    title={Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN},
    author={Benjamin Parlier, Lou Sala"un, Hong Yang},
    journal={arXiv preprint arXiv:2406.04456},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04456},
    primaryClass={eess.SP cs.AI cs.LG}
}