Application of Mobility Prediction in Wireless Networks Using Markov Renewal Theory

An understanding of the network traffic behavior is essential in the evolution of today´s wireless networks and thus leads to a more efficient planning and management of the network´s scarce bandwidth resources. Prior reservation of radio resources at future locations of a user´s mobile trajectory can assist in optimizing the allocation of the network´s limited resources and sustaining a desirable quality-of-service (QoS) level. This can also help to ensure that the network service can be available anywhere and anytime, which is only possible if, at any time, we can predict from where a user is going to make its demands. In this paper, we apply Markov renewal processes for both mobility modeling and predicting the likelihoods of the next-cell transition, along with anticipating the duration between the transitions, for an arbitrary user in a wireless network. Our proposed prediction technique will also be extended to compute the likelihoods of a user being in a particular state after N transitions. The proposed technique can also be used to estimate the expected spatial-temporal traffic load and activity at each location in a network´s coverage area. Using some real traffic data, we illustrate how our proposed prediction method can lead to a significant improvement over some of the conventional methods.