A temporal graph neural network treats ISAC delay-Doppler maps as evolving graphs and solves multi-target tracking as temporal node classification, yielding lower NMSE than a Kalman filter baseline in ray-tracing simulations.
Predictive ISAC beamforming using LoRA-Transformer LSTM integrated with GNN in ultra-dense D2D mmWave networks,
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Temporal Graph Neural Network for ISAC Target Detection and Tracking
A temporal graph neural network treats ISAC delay-Doppler maps as evolving graphs and solves multi-target tracking as temporal node classification, yielding lower NMSE than a Kalman filter baseline in ray-tracing simulations.