GNN-Based Global CSI Reconstruction for Fronthaul-Limited Distributed MIMO Systems

2026-09-20

AI Summary

本文针对分布式MIMO系统中全信道状态信息获取导致的前传开销问题，提出了一种基于图神经网络重构和任务驱动天线选择的方法来减少前传需求。

Abstract

Global channel state information (CSI) acquisition is essential for cooperative precoding in distributed multiple-input multiple-output (DMIMO) systems, but uploading full instantaneous CSI from all distributed antennas creates heavy fronthaul overhead. This paper proposes a fronthaul-efficient acquisition framework based on graph neural network (GNN) reconstruction and task-driven antenna selection. Each transmission and reception point (TRP) uploads only selected antenna CSI, while the centralized unit (CU) reconstructs the full global CSI from partial observations. A universal mask-conditioned GNN is trained with random upload masks, used to evaluate antenna subsets under a fronthaul budget, and then fine-tuned for the selected deployment mask. Simulation results show improved CSI reconstruction accuracy with lower fronthaul and pilot overhead.

Problem

• Global CSI
• Distributed MIMO
• Fronthaul Overhead

Innovation

• graph neural network
• task-driven antenna selection
• fronthaul-efficient acquisition
• CSI reconstruction
• distributed MIMO