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.
Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems,
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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.