How to Improve Learning Efficiency of GNN for Precoding?

Learning precoding with deep neural networks (DNNs) enables real-time implementation and robustness to imperfect channels. However, existing DNNs for learning precoding suffer from high training complexity and weak generalization ability to problem scales, which impedes their practical use in wireless systems with user scheduling. In this paper, we propose a graph neural network (GNN) to learn precoding policies efficiently by resorting to the model of Taylor's expansion of matrix pseudo-inverse. The GNN can capture the importance of neighbored edges when aggregating their information, which is critical for improving the learning efficiency. We also interpret the role of the trainable parameters on learning the powers and directions of the precoding vectors. Simulation results show that the proposed model-driven GNN can well-learn spectral and energy efficient precoding policies with low training complexity, and is generalizable to the numbers of users.