Lightweight CNN with separable convolutions, hierarchical augmentation and power-based label smoothing reaches 84% cross-environment beam prediction accuracy on two real DeepSense 6G scenarios while cutting parameters by 52x and complexity by 79x versus ResNet.
Knowledge distillation for sensing-assist ed long- term beam tracking in mmWave communications
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abstract
Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features, while gated recurrent units with attention capture the temporal dependencies within sequences. The large NN then acts as the teacher to guide the training of a lightweight student NN via knowledge distillation. The student requires shorter input sequences yet preserves long-term beam prediction ability. Numerical results demonstrate that the teacher achieves Top-5 accuracies exceeding 93% for current and six future time slots, approaching state-of-the-art performance with a 90% reduction of model parameters. The student closely matches the teacher's performance while reducing the number of model parameters by over 1670% and cutting complexity by over 450%, despite operating with 60% shorter input sequences. This improvement significantly enhances data efficiency, reduces latency, and reduces power consumption in sensing and processing.
fields
eess.SP 2years
2026 2representative citing papers
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.
citing papers explorer
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Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications
Lightweight CNN with separable convolutions, hierarchical augmentation and power-based label smoothing reaches 84% cross-environment beam prediction accuracy on two real DeepSense 6G scenarios while cutting parameters by 52x and complexity by 79x versus ResNet.
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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.