LDM-satellite: A new scheme for packet loss classification over LEO satellite network

Abstract

The packet loss classification has always been a hot and difficult issue in TCP congestion control research. Compared with the terrestrial network, the probability of packet loss in LEO satellite network increases dramatically. What's more, the problem of concept drifting is also more serious, which greatly affects the accuracy of the loss classification model. In this paper, we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks, named LDM-Satellite, which consists of three modules: concept drift detection, lost packet cache and hybrid integration classification. As far, this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks. We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier. And a new weight algorithm for these classifiers is given. In ns-2 simulation, LDM-Satellite has a better AUC (0.9885) than the single-model machine learning classification algorithms. The accuracy of loss classification even exceeds 98%, higher than traditional TCP protocols. Moreover, compared with the existing protocols used for satellite networks, LDM-Satellite not only improves the throughput rate but also has good fairness.