Addressing the Influence of Hidden State on Wireless Network Optimizations Using Performance Maps

Abstract

Performance of wireless connectivity for network client devices is location dependent. It has been shown that it can be beneficial to collect network performance metrics along with location information to generate maps of the location dependent network performance. These performance maps can be used to optimize wireless networks by predicting future network performance and scheduling the network communication for certain applications on mobile devices. However, other important factors influence the performance of the wireless communication such as changes in the propagation environment and resource sharing. In this work we extend the framework of performance maps for wireless networks by introducing network state as an abstraction for all other factors than location that influence the performance. Since network state might not always be directly observable the framework is extended with a network state estimation and prediction function. In the evaluation scenario, resource sharing is used as an example of network state and the framework is applied to the use-case of scheduling TCP-based communication to lower communication overhead. Using extensive simulations for evaluation we show how dynamic network state caused by resource sharing influences the scheduling performance.