A class of mobile motion prediction algorithms for wireless mobile computing and communication
Mobile Networks and Applications - Special issue: routing in mobile communications networks
Wcdma for Umts
A Practical User Mobility Prediction Algorithm for Supporting Adaptive QoS in Wireless Networks
ICON '99 Proceedings of the 7th IEEE International Conference on Networks
Location prediction algorithms for mobile wireless systems
Wireless internet handbook
On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication
IEEE Transactions on Mobile Computing
HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications
HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications
Mobility prediction based on an ant system
Computer Communications
NSWCTC '09 Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 01
Mobility-Aware distributed topology control for mobile multi-hop wireless networks
ICOIN'06 Proceedings of the 2006 international conference on Information Networking: advances in Data Communications and Wireless Networks
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
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As wireless networks have been widely deployed for public mobile services, predicting the location of a mobile user in wireless networks became an interesting and challenging problem. If we can predict the next cell which the mobile users are going to correctly, the performance of wireless applications, such as call admission control, QoS and mobility management, can be improved as well. In this paper, we propose a mobility prediction algorithm based on dividing sensitive ranges. The division is in accordance with the cell transform probability. Then different prediction methods are applied according to the sensitivity of the range to gain high precision. Simulations are conducted to evaluate the performance of the proposed scheme. As it turns out, the simulation results show that the proposed scheme can accurately predict the location for mobile users even in the situation of lacking location history.