An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Predictive distance-based mobility management for multidimensional PCS networks
IEEE/ACM Transactions on Networking (TON)
Handoff Prediction by Mobility Characteristics in Wireless Broadband Networks
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
Wireless Communications
A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps
IEEE Transactions on Mobile Computing
On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication
IEEE Transactions on Mobile Computing
Evaluating Filtering Strategies for Decentralized Handover Prediction in the Wireless Internet
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Impact of Handover on VoIP Speech Quality in WiMAX Networks
ICN '09 Proceedings of the 2009 Eighth International Conference on Networks
Optimal admission control in multimedia mobile networks with handover prediction
IEEE Wireless Communications
QoS provisioning in cellular networks based on mobility prediction techniques
IEEE Communications Magazine
Computer Networks: The International Journal of Computer and Telecommunications Networking
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One of the most important challenges in mobile wireless networks is to provide full mobility together with minimum degradation of quality of service. This can be ensured by handover prediction. Handover prediction means a determination of the next station that will serve a mobile station. This paper proposes a prediction technique based on monitoring the signal quality between the mobile station and all base stations in its neighborhood. The proposed technique utilizes two different thresholds for selection of the most likely target base station. Further, the potential improvement of the prediction efficiency via techniques originally proposed for minimizing the number of redundant handovers is analyzed. The efficiency of the proposed prediction technique is evaluated and compared with other prediction techniques based on channel characteristics in scenarios according to IEEE 802.16m. The proposed technique achieves a prediction hit rate of up to 93%.