Joint Power Control and Precoding for Cell-Free Massive MIMO Systems With Sparse Multi-Dimensional Graph Neural Networks

TL;DR

This paper introduces a sparse multi-dimensional graph neural network framework for cell-free massive MIMO systems, reducing computational complexity while maintaining high spectral efficiency, thus facilitating practical large-scale deployment.

Contribution

It proposes a novel sparse graph neural network approach for joint power control and precoding, balancing performance and complexity in CF mMIMO systems.

Findings

Sparse method reduces computational complexity significantly.

Performance degradation is minimal with the sparse approach.

Simulation confirms effective trade-off between complexity and performance.

Abstract

Cell-free massive multiple-input multiple-output (CF mMIMO) has emerged as a prominent candidate for future networks due to its ability to significantly enhance spectral efficiency by eliminating inter-cell interference. However, its practical deployment faces considerable challenges, such as high computational complexity and the optimization of its complex processing. To address these challenges, this correspondence proposes a framework based on a sparse multi-dimensional graph neural network (SP-MDGNN), which sparsifies the connections between access points (APs) and user equipments (UEs) to significantly reduce computational complexity while maintaining high performance. In addition, the weighted minimum mean square error (WMMSE) algorithm is introduced as a comparative method to further analyze the trade-off between performance and complexity. Simulation results demonstrate that the sparse method achieves an optimal balance between performance and complexity, significantly reducing the computational complexity of the original MDGNN method while incurring only a slight performance degradation, providing insights for the practical deployment of CF mMIMO systems in large-scale network.