Macromobility prediction for high-priority resource reservation in wireless networks

Efficient management of mobile network resources is a critical task for successful operation. Giving higher priority and quality of service to applications with a high return on invest value, demands for intelligent distribution of bit rates and radio access. The research approach proposed here is based on the fact, that users cannot move freely, but are restricted to streets or rails in position, direction and speed. Following the geographical topology while in motion some locations generate a unique signature by forcing the user to traverse similar sequences of base stations. The signatures can be revealed by applying pattern detection methods on the historical user movements, allowing to predict future positions of the user on a large time scale, especially to reserve resources for rescue workers. In this paper we discuss the effect of different attributes about the user's movement for the prediction quality on different pattern detection algorithms in order to improve and accelerate the process of rescue missions. Further the availability of these attributes will be discussed for different scenarios and validated by traces from an actual large scale HSDPA/GSM network.