Kalman filter based bandwidth estimation and predictive flow distribution for concurrent multipath transfer in wireless networks

More and more terminals come equipped with multiple network interfaces, usually connected to different wireless networks. Connected multiple wireless networks have varying link characteristics. Because of unreliable wireless links, we need an efficient bandwidth estimation to describe link characteristics. The Kalman filter is an efficient recursive method, which not only estimates and corrects the current system states but also predicts even the future states based on the latest state. In this paper, we use the Kalman filter to estimate available bandwidth in wireless networks with different loss rates. The predictive arrival time is calculated for each packet before it is transferred. Based on this, we proposed a predictive flow distribution algorithm for concurrent multipath transfer in wireless networks. The simulation results show that our predictive flow distribution algorithm improves congestion window growth pattern by reducing the out-of-order packets. As a result, the total throughput increases under different random wireless link loss conditions.

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