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Network Selection Algorithm Based on Chi-Square Distance in Heterogeneous Wireless Networks

  • Hewei YuEmail author
  • Lihua Chen
  • Jingxi Yu
Article
  • 7 Downloads

Abstract

This paper proposes an access network selection algorithm based on a cooperative strategy with Chi square distance, subjective weights for traffic classes and objective attributes of network. The algorithm gets the subjective weights of attribute in four traffic classes using AHP at first, then calculates objective weights using a cooperative method of entropy weight and criteria importance though intercriteria correlation. The final weights are obtained by a comprehensive weighting method based on minimizing the deviation between weights. Finally we measure the Chi square distance between every candidate network and the ideal network and choose the one which has the minimum Chi square distance to the idea network as the best access network. Simulation results show that the proposed algorithm can select the most suitable access network to meet the requirements of current traffic and observably extend the discernibility between candidate networks. The ratio between the best and the second-best network in this algorithm is 8 or 9 times bigger than that in technique for order preference by similarity to ideal solutions, simple additive weighting and grey relational analysis. Furthermore, it can avoid falling into local optimal situation, choose the correct optimal access network when some network attributes appear extreme data, and has good algorithm robustness.

Keywords

Access network selection Chi square distance Heterogeneous network CRITIC Entropy weight AHP 

Notes

Acknowledgements

This study is supported by Natural Science Foundation of Guangdong Province (2017A030307035), Innovative Research Project of the Education Department of Guangdong Province (2017KTSCX127), Key Collaborative Innovation Project among Industry, University and Institute of Guangzhou (201604010001).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.Huawei Technologies Co., Ltd.ShenzhenPeople’s Republic of China
  3. 3.Department of MathematicsUniversity of WashingtonSeattleUSA

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