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Reconfiguration Decision Making Based on Ant Colony Optimization in Cognitive Radio Network

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Abstract

Facing to the challenges of dynamic adaptation capabilities in the time-varying environment of cognitive radio networks (CRN), reconfiguration capabilities are introduced to flexibly and dynamically adapt to changing wireless environment and service requirement. As one of the essential characteristics for CRN, the cognitive reconfiguration can meet the user requirements, realize interoperability between heterogeneous networks, make full use of radio resources and adapt to the time-varying environment to achieve the end-to-end requirements. However, the reconfiguration implementation is still challenging due to its need for complex environment cognition and multi-objects optimization. In this direction, ant colony optimization(ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the appropriate model of the reconfiguration decision making process to achieve the adaption alternatives. The aim of this paper is to present a generic cognitive reconfiguration framework including indispensable function entities for autonomous reconfiguration decision making with regard to the multiple and complex objectives. Moreover, three kinds of reconfiguration approaches, which are parameters reconfiguration, radio resource reconfiguration and heterogeneous access reconfiguration, are proposed. Finally, numerous results prove the effective performance improvements of ACO based reconfiguration solution in CRN.

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Authors and Affiliations

  1. Wireless Technology Innovation Institute (WTI) and Key Laboratory of Universal Wireless Communications Ministry of Education, Information and Telecommunication Engineering of Beijing University of Posts and Telecommunications (BUPT), Haidian Dist., Xitucheng Rd, Beijing, 100876, People’s Republic of China

    Qian He, Zhiyong Feng & Ping Zhang

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  1. Qian He
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  2. Zhiyong Feng
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  3. Ping Zhang
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Correspondence to Qian He.

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He, Q., Feng, Z. & Zhang, P. Reconfiguration Decision Making Based on Ant Colony Optimization in Cognitive Radio Network. Wireless Pers Commun 71, 1247–1269 (2013). https://doi.org/10.1007/s11277-012-0872-3

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