Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications

Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context, infrastructure-mounted cameras can capture rich environmental information that can facilitate beam tracking design. In this work, we develop an efficient attention-enhanced machine learning model for long-term beam tracking built upon convolutional neural networks and gated recurrent units to predict both current and future beams from past observed images. The integrated temporal attention mechanism substantially improves its predictive performance. Numerical results demonstrate that the proposed design achieves Top-5 beam prediction accuracies exceeding 90% across both current and six future time slots, significantly reducing overhead arising from sensing and processing for beam training. It further attains 97% of state-of-the-art performance with only 3% of the computational complexity.