Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN

• URL: http://arxiv.org/abs/2406.04456v1
• Date: Thu, 6 Jun 2024 19:29:33 GMT
• Title: Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN
• Authors: Benjamin Parlier, Lou Salaün, Hong Yang,
• Abstract summary: We develop a graph neural network (GNN) to compute, within a time budget of 1 to 2 milliseconds required by practical systems. We show that it achieves near optimal spectral efficiency in a range of scenarios with different number of APs and UEs.
• Score: 15.271970287767164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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