This letter proposes a graph neural network (GNN)-based framework for statistical precoder design that leverages model-based insights to compactly represent statistical knowledge, resulting in efficient, lightweight architectures. The framework also supports approximate statistical information in frequency division duplex (FDD) systems obtained through a Gaussian mixture model (GMM)-based limited feedback scheme in massive multiple-input multiple-output (MIMO) systems with low pilot overhead. Simulations using a spatial channel model and measurement data demonstrate the effectiveness of the proposed framework. It outperforms baseline methods, including stochastic iterative algorithms and Discrete Fourier transform (DFT) codebook-based approaches, particularly in low pilot overhead systems.
@article{arxiv.2412.07519,
title = {Statistical Precoder Design in Multi-User Systems via Graph Neural Networks and Generative Modeling},
author = {Nurettin Turan and Srikar Allaparapu and Donia Ben Amor and Benedikt Böck and Michael Joham and Wolfgang Utschick},
journal= {arXiv preprint arXiv:2412.07519},
year = {2024}
}