Real-time throughput prediction for cognitive Wi-Fi networks

Wi-Fi as a wireless networking technology has become a widely acceptable commonplace. Over the course of time, the applications landscape of Wi-Fi networks is growing tremendously. The proliferation of new services is driving the industry to adopt novel and agile approaches to ensure the quality of experience delivered to the end user. To enhance end user experience, transmission throughput is an important metric that has a strong impact on the end-user quality of experience. The accurate real-time prediction of throughput can bring several new possibilities to enhance user experience in future self-organizing cognitive networks. However the real-time prediction of transmission throughput is challenging due to the dependency on several parameters. Previous studies on throughput prediction are primarily focused on non real-time prediction in less-dynamic networks. The studies also do not provide high accuracy as required in cognitive networks for efficient decision making. The purpose of this study is to use data-driven machine learning (ML) techniques and evaluating their accuracy and efficiency to predict the transmission throughput in Wi-Fi networks. Four algorithms are used namely multi-layer perceptrons (MLP), support vector regressors (SVR), decision trees (DT) and random forests (RF). It is widely understood that the accuracy and efficiency of machine learning (ML) algorithms hugely depend upon the datasets being used to train the model. Hence, this study proposes two distinct data models for creating ML-ready datasets using feature engineering. The accuracy of each ML algorithm over these datasets is evaluated. The evaluation results show a maximum prediction accuracy of 96.2% using MLP algorithm, followed by DT (94.5%), RF (93.3%) and SVR (91.0%) respectively. Furthermore, the complexity analysis is also presented to support the adaptation of such schemes in real-time applications.