基于图神经网络的Cell-Free MIMO通感一体化波束成形框架
Resilient Cell-Free MIMO ISAC Under Uncertainties: An Impairment-Aware Graph Neural Network Framework
一篇通信方向的论文：用图神经网络做通感一体化波束成形，单个模型能同时扛住信道误差和杂波，速率保住各专用基线的约97%。
arXiv 论文提出一种损伤感知的异构图神经网络（GNN），用于 cell-free MIMO 系统中通感一体化（ISAC）的波束成形设计。该框架将 CSI 误差水平与杂波协方差作为边特征输入，使单个训练后的模型能同时适应两类不确定性。训练采用基于杂波感知信干噪比的最低用户速率低分位数，并通过归一化 Tchebycheff 标量化进行平衡。仿真显示该 GNN 在仅感知和仅通信的基线对比中，单个模型可达到各基线约 97% 的速率，同时同时服务两种功能，并能泛化到未见过的 CSI 误差水平和杂波数量。
Resilient Cell-Free MIMO ISAC Under Uncertainties: An Impairment-Aware Graph Neural Network Framework
Beamforming designs for integrated sensing and communication (ISAC) in cell-free multiple-input multiple-output (MIMO) systems face the challenge to stay reliable under uncertainties from both channel state information (CSI) as well as clutter. To this end, this paper proposes an impairment-aware heterogeneous graph neural network (GNN) framework for beamforming that obtains the CSI error level and the clutter covariance as edge features, which enables a single trained model to adapt to both impairments. In particular, the GNN is trained on a low quantile of the minimum user rate with a clutter-aware signal-to-clutter-plus-noise ratio, balanced by a normalized Tchebycheff scalarization. Simulation results show that the proposed GNN framework outperforms the considered baselines in sensing-only and communication-only settings. Depending on the operating point, a single GNN model attains approximately 97\% of each baseline's rate while serving both functionalities at once. The model further generalizes to unseen CSI error levels and clutter counts and renders the ISAC system resilient.