A ResNet34 network trained on network digital twin data with max-pooling downsampling and AWGN augmentation improves LoS/NLoS classification accuracy by 5-10% and reduces inference FLOPs by 98.55% versus the SegNet baseline.
Toward digital network twins: Integrating sionna RT in ns-3 for 6G Multi-RAT networks simulations,
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AI-empowered Real-Time Line-of-Sight Identification via Network Digital Twins
A ResNet34 network trained on network digital twin data with max-pooling downsampling and AWGN augmentation improves LoS/NLoS classification accuracy by 5-10% and reduces inference FLOPs by 98.55% versus the SegNet baseline.