An intelligent prediction model for generating LGD trigger of IEEE 802.21 MIH

Authors:
M. Yousaf;Sohail Bhatti;Maaz Rehan;A. Qayyum;S. A. Malik
Affiliations:
Center of Research in Networks and Telecommunications, M. A. Jinnah University, Islamabad, Pakistan;Center of Research in Networks and Telecommunications, M. A. Jinnah University, Islamabad, Pakistan;Center of Research in Networks and Telecommunications, M. A. Jinnah University, Islamabad, Pakistan;Center of Research in Networks and Telecommunications, M. A. Jinnah University, Islamabad, Pakistan;Center of Research in Networks and Telecommunications, M. A. Jinnah University, Islamabad, Pakistan
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

IEEE recently standardized 802.21-2008 Media Independent Handover (MIH) standard. MIH is a key milestone toward the evolution of integrated heterogeneous 4G wireless networks. MIH provides number of link layer events in a unified way that facilitate upper layer protocols in making handover decisions. One such event is Link Going Down (LGD) trigger. LGD is a predictive event that is generated when link conditions are expected to degrade in near future. Traditionally such link quality degradations and connectivity losses are predicted on the basis of a single parameter only i.e. received signal strength. However, in varying wireless conditions, simple predictions relying on single link layer parameter may generate false LGD triggers. This false triggering may initiate unnecessary handovers that rather than facilitating upper layer mobility management protocols, may cause overhead and may degrade the overall network performance. In this paper, we present an intelligent model for generating MIH LGD trigger reliably. In our implementation, we used 'Time Delay Neural Networks (TDNN)' approach using multiple link layer parameters for LGD predictions. We also analyzed the prediction accuracy and the feasibility of using such intelligent technique for mobile devices.