  Learning-based precoding has been shown able to be implemented in real-time,
jointly optimized with channel acquisition, and robust to imperfect channels.
Yet previous works rarely explain the design choices and learning performance,
and existing methods either suffer from high training complexity or depend on
problem-specific models. In this paper, we address these issues by analyzing
the properties of precoding policy and inductive biases of neural networks,
noticing that the learning performance can be decomposed into approximation and
estimation errors where the former is related to the smoothness of the policy
and both depend on the inductive biases of neural networks. To this end, we
introduce a graph neural network (GNN) to learn precoding policy and analyze
its connection with the commonly used convolutional neural networks (CNNs). By
taking a sum rate maximization precoding policy as an example, we explain why
the learned precoding policy performs well in the low signal-to-noise ratio
regime, in spatially uncorrelated channels, and when the number of users is
much fewer than the number of antennas, as well as why GNN is with higher
learning efficiency than CNNs. Extensive simulations validate our analyses and
evaluate the generalization ability of the GNN.
