Traffic allocation control using support vector machine in heterogeneous wireless link aggregation

In heterogeneous wireless network environment, various radio access networks (RANs) are available for mobile terminals which have multiple wireless network interfaces to connect to each different RAN. However, it is very difficult to improve the throughput by aggregating different RANs, which have different quality of service (QoS) in the throughput, the delay, the packet loss, and so on. In this paper, we propose a traffic allocation control scheme using a machine learning algorithm to improve the link aggregation throughput in such heterogeneous wireless networks. We introduce the support vector machine, which has an advantage on generalization to have good performance for unknown input patterns. By our experiments using a cognitive wireless network system, called Cognitive Wireless Clouds, it is clarified that our proposed scheme improves the throughput higher than the conventional methods based on the throughput of each RAN.