A token-routing multi-modal transformer reduces inference latency by 86.2%, GPU memory by 35%, and FLOPs by 80% for beamforming tasks with negligible accuracy loss while enabling proactive handover on a real testbed dataset.
Resource-ef ficient beam prediction in mmwave communications with multimodal r ealistic simulation framework
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Knowledge distillation produces compact student models that match large teacher models in mmWave beam prediction accuracy from sub-6 GHz channels while cutting parameters and complexity by 99%.
Knowledge distillation creates a lightweight student model that reaches over 96% top-5 beam prediction accuracy on real multimodal sensor data while using 27 times fewer parameters than the teacher.
Knowledge distillation creates a compact neural network for long-term beam tracking in mmWave communications that matches a larger teacher's accuracy with far fewer parameters and shorter input sequences.
citing papers explorer
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Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks
A token-routing multi-modal transformer reduces inference latency by 86.2%, GPU memory by 35%, and FLOPs by 80% for beamforming tasks with negligible accuracy loss while enabling proactive handover on a real testbed dataset.
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Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels
Knowledge distillation produces compact student models that match large teacher models in mmWave beam prediction accuracy from sub-6 GHz channels while cutting parameters and complexity by 99%.
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Knowledge Distillation for Lightweight Multimodal Sensing-Aided mmWave Beam Tracking
Knowledge distillation creates a lightweight student model that reaches over 96% top-5 beam prediction accuracy on real multimodal sensor data while using 27 times fewer parameters than the teacher.
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Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Knowledge distillation creates a compact neural network for long-term beam tracking in mmWave communications that matches a larger teacher's accuracy with far fewer parameters and shorter input sequences.