标题： 基于稀疏多维图神经网络的无蜂窝大规模MIMO系统的联合功率控制与预编码 显示英文标题

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

摘要： 无基站大规模多输入多输出（CF mMIMO）由于能够通过消除小区间干扰显著提高频谱效率，已成为未来网络的突出候选方案。然而，其实际部署面临诸多挑战，如计算复杂度高和复杂处理的优化。为解决这些挑战，本文提出了一种基于稀疏多维图神经网络（SP-MDGNN）的框架，通过稀疏化接入点（APs）和用户设备（UEs）之间的连接，显著降低计算复杂度，同时保持高性能。此外，引入加权最小均方误差（WMMSE）算法作为对比方法，以进一步分析性能与复杂度之间的权衡。仿真结果表明，稀疏方法在性能与复杂度之间实现了最佳平衡，显著降低了原始MDGNN方法的计算复杂度，仅造成轻微的性能下降，为CF mMIMO系统在大规模网络中的实际部署提供了见解。 显示英文摘要

摘要： 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.