CapRadar: Real-time adaptive bandwidth prediction for dynamic wireless networks

Abstract

With the emergence of 4G/5G cellular networks, mobile Internet is becoming increasingly popular and numerous mobile applications have emerged, which puts higher demands on the accuracy of predicting available bandwidth. However, compared to WiFi and wired networks, the links of 4G/5G cellular networks are highly dynamic, and the unstable nature of their network environments makes the bandwidth trajectories vary. Traditional bandwidth prediction models rarely consider the bandwidth characteristics of various network scenarios, and a single prediction model is difficult to be applied to all scenarios, which makes it challenging to achieve high accuracy of available bandwidth prediction. To solve the above problems, we propose a real-time bandwidth prediction method called CapRadar. The method classifies bandwidth into scenarios and matches the optimal prediction model for each type of scenario, i.e., switches the prediction model in real time according to the changes of the scenario. Specifically, we first extract the statistical characteristics of the bandwidth using statistical method, and based on the extracted characteristics, a SVM classifier is used to detect different network scenarios. After that, the best prediction model is matched for them in the algorithm library based on the scenario classification results. The experimental results show that CapRadar can reduce the root mean square error (RMSE) by about 18.9% and the mean error (MAE) by about 21.5%. For practical applications, we use a pre-trained SVM model for real-time scenario detection, and then we can dynamically switch the prediction model.