AIBullisharXiv – CS AI · 6h ago6/10
🧠
Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction
Researchers introduce Lightweight PCGAE-Net, a new neural network architecture that reduces 5G channel prediction model size by 58% while improving accuracy by up to 6.0dB. The model addresses architectural inefficiencies in existing transformers through parallel attention mechanisms and a bottleneck autoencoder, enabling deployment on base-station hardware with computational constraints.