The shadow cluster concept for resource allocation and call admission in ATM-based wireless networks
MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
A class of mobile motion prediction algorithms for wireless mobile computing and communication
Mobile Networks and Applications - Special issue: routing in mobile communications networks
A predictive bandwidth reservation scheme using mobile positioning and road topology information
IEEE/ACM Transactions on Networking (TON)
Computers & Mathematics with Applications
Mobility prediction using future knowledge
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
A hierarchical prediction model for two nodes-based IP mobile networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Predictive mobility support for QoS provisioning in mobile wireless environments
IEEE Journal on Selected Areas in Communications
Mobility prediction in mobile wireless networks
Journal of Network and Computer Applications
Combining local and global profiles for mobility prediction in LTE femtocells
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Femtocells sharing management using mobility prediction model
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
Location Prediction Based on a Sector Snapshot for Location-Based Services
Journal of Network and Systems Management
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Mobility prediction is an important solution to enable seamless handovers in cellular networks and the mobility trace is the main information used to perform it. However, using solely this information makes the prediction process difficult when the mobile user is new in the network, that is, when its mobility trace is poor. In this paper, we investigate a Markov-based prediction model which focuses on new mobile users behaviour prediction. In order to assess our approach, we use data sets of a real cellular network in a major US urban area. The efficiency of the prediction model relies on both the ability of the model to predict successfully the next move of a mobile user and its ability to perform such a prediction in a short delay. Comparing our approach with previous solutions, we show that our solution outperforms in all cases the previous solutions and essentially succeeds to make better predictions for new mobile users.