Large Model Framework for Wireless Network Optimization in Smart Physical Education Environments

Modern physical education increasingly relies on wireless communication networks to deliver immersive training experiences through wearable devices, motion-tracking sensors, and real-time performance analytics. However, optimizing wireless network performance in dynamic physical education environments presents complex challenges due to rapidly changing user mobility patterns, varying signal interference from athletic equipment, and fluctuating bandwidth demands during different exercise activities. This letter proposes a novel wavelet-enhanced large model framework that integrates wavelet transform signal processing with enhanced position encoding in transformer architectures to predict and optimize wireless network performance for physical education applications. Experimental validation demonstrates that our proposed model accurately captures non-stationary behavior and abrupt changes in wireless network performance during various physical activities. The RMSE and MAPE metrics show improvements of 29.9% and 2.9%, respectively, compared to baseline transformer models, and 34.5% and 3.4% improvements compared to LSTM approaches, providing a novel technical solution for smart physical education network management.