1 related articles
The paper proposes an impairment-aware heterogeneous graph neural network framework that treats CSI errors and clutter covariance as edge features to achieve robust beamforming in cell-free MIMO ISAC systems, with a single model reaching about 97% of baseline rate and generalizing to unseen impairment levels.
