Transmission Opportunity and Throughput Prediction for WLAN Access Points via Multi-Dimensional Feature Modeling
Wei Li, Huang Xin, Danju Lv, Yueyun Yu, Yan Zhang, Zhicheng Zhu, Ting Zhou · Electronics · 2025
With the rapid development of wireless communication, Wireless Local Area Networks (WLANs) are widely deployed in high-density environments. Ensuring fast handovers and optimal AP selection during device roaming is critical for maintaining network throughput and user experience. However, frequent mobility, high access density, and dynamic channel fluctuations complicate throughput prediction. To address this, we propose a method combining the Snow-Melting Optimizer (SMO) with decision tree regression models to optimize feature selection and model transmission opportunities (TXOP) and AP throughput. Experimental results show that the Extreme Gradient Boosting (XGBoost) model performs best, achieving high prediction accuracy for TXOP (MSE = 1.3746, R2 = 0.9842) and AP throughput (MAE = 2.5071, R2 = 0.9896). This approach effectively captures the nonlinear relationships between throughput and network factors in dense WLAN scenarios, demonstrating its potential for real-world applications.