# Statistical Precoder Design in Multi-User Systems via Graph Neural Networks and Generative Modeling

arXiv: https://arxiv.org/abs/2412.07519 · HTML: https://mlsanity.com/paper/2412.07519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Precoder Design in Multi-User FDD Systems with VQ-VAE and GNN](https://mlsanity.com/paper/2510.09495/) (2025)

## Abstract (arXiv)

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.