Analysis of Wi-Fi performance data for a Wi-Fi throughput prediction approach
Pan Dan · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017
Due to low cost and portability of Wi-Fi technologies, wireless network deployment has been widely accepted in the residential environment. The evaluation results of customers’ home wireless network performance level provides a reference for operators to improve their network capacity in order to face the emerging requirement of Wi-Fi service. However, the dynamic nature of Wi-Fi network makes Wi-Fi performance analysis difficult to perform. In this thesis, a Wi-Fi parameter visualization tool is implemented to show users’ Wi-Fi performance in a graphic way. This tool could help operators investigate customers’ Wi-Fi environment to see if performance degradation exists or not. Besides, a machine learning method is used for Wi-Fi performance analysis to predict Wi-Fi throughput. A SVM-based classification model is proposed to work as a prediction function. This function takes Wi-Fi parameters both for target AP and nearby interference APs as input, and output is categorized Wi-Fi throughput, good, medium, poor or very poor. Different SVM kernel functions conducted to evaluate the proposed model and results show that classification accuracy can be up to 0.88. It demonstrates that Wi-Fi throughput could be classified using a simple measurement way and limited Wi-Fi physical parameters.