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

Nurettin Turan; Srikar Allaparapu; Donia Ben Amor; Benedikt Böck; Michael Joham; Wolfgang Utschick

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arXiv · 2412.07519

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

Nurettin Turan·Srikar Allaparapu·Donia Ben Amor·Benedikt Böck·Michael Joham·Wolfgang Utschick

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Abstract

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.

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Nurettin Turan, Srikar Allaparapu, Donia Ben Amor, Benedikt Böck, Michael Joham, Wolfgang Utschick. 2024-12-10. Statistical Precoder Design in Multi-User Systems via Graph Neural Networks and Generative Modeling. https://arxiv.org/abs/2412.07519

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