Handover Management in Enhanced MIH Framework for Heterogeneous Wireless Networks Environment

Authors:
Ying Wang;Ping Zhang;Yun Zhou;Jun Yuan;Fang Liu;Gen Li
Affiliations:
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
Wireless Personal Communications: An International Journal
Year:
2010

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Abstract

Vertical handover decision making is one of the key problems in heterogeneous networks environment. In IEEE 802.21 standard, a Media Independent Handover (MIH) framework is proposed to improve user experience of mobile devices by facilitating handover in heterogeneous networks with measurements and triggers from link layers. However, vertical handover decision making can benefit from the information more than link layers. In this paper, an Enhanced Media Independent Handover (EMIH) framework is proposed by integrating more information from application layers, user context and network context. Given such information, there is also another important problem on how to select a favorite network. Two quite important problems from realistic scenario are as follows: (1) how to make use of partial knowledge due to incomplete value measurement on decision factors; (2) how to deal with robustness problem due to inaccurate measurement on decision factors. In order to tackle these problems, two novel Weighted Markov Chain (WMC) approaches based on rank aggregation are proposed in this paper, in which a favorite network is selected as the top one of rank aggregation result fused from multiple ranking lists based on decision factors. Moreover, an entropy weighting method, combined with WMC approach, is studied. The simulations demonstrate the effectiveness of these proposed approaches.