Abstract
Facing the challenges of dynamic adaptation capabilities in the time-varying environment of cognitive wireless networks (CWNs), we introduce reconfiguration capabilities that flexibly and dynamically adapt to changing wireless environments and service requirements. As an essential characteristic of CWNs, the cognitive reconfiguration can meet user requirements, realize interoperability between heterogeneous networks, make full use of radio resources and adapt to time-varying environments to achieve end-to-end requirements. However, the reconfiguration implementation is still challenging due to the need for complex environment cognition, multi-objective optimization, autonomic decision-making and end-to-end requirement extraction. As an intelligent technology for solving complex issues, we apply adaptive neuro-fuzzy inference system (ANFIS) techniques in this paper to address these challenges in cognitive reconfiguration for self-learning and optimal decision making based on multi-domain cognition results. Moreover, this paper designs a generic ANFIS cognitive reconfiguration system including three functional entities, which are the context management module, multi-domain database and ANFIS optimization module. Finally, numerous results prove the effective performance improvements of the ANFIS based reconfiguration solution in CWN for global end-to-end goals.
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Mitola J, Maquire J, Gerald Q. Cognitive radio: Making software radios more personal. IEEE Pers Commun, 1999, 6: 13–18
Nicola B, Michele Z. Fuzzy logic for cross-layer optimization in cognitive radio networks. IEEE Commun Mag, 2008, 46: 64–71
Demestichas P, Dimitrakopoulos G, Strassner J, et al. Introducing reconfigurability and cognitive networks concepts in the wireless world. IEEE Veh Technol Mag, 2006, 1: 32–39
Thomas R W Friend D H Dasilva L A, et al. Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives. IEEE Commun Mag, 2006, 44: 51–57
George D, Flora M, Karim E K, et al. Scenarios, System Requirements and Roadmaps for Reconfigurability. Technical Report. IST Wireless World Research Forum, 2004
Feng Z Y Zhang Q X Tian F, et al. Novel research on cognitive pilot channel in cognitive wireless network. Wireless Pers Commun, 2012, 62: 455–478
Muck M, Bourse D, Moessner K, et al. End to end reconfiguability in heterogeneous wireless systems-software and cognitive radio solutions enriched by policy- and context-based decision making. In: Proceedings of the 16th IST Mobile and Wireless Communications Summit, 2007 Jul 1–5, Budapest, Hungary. Washington DC: IEEE, 2007. 1–5
Boufidis Z, Alonistioti N, Holland O, et al. End-to-end architecture for cognitive reconfigurable wireless networks. In: Proceedings of the 16th IST Mobile and Wireless Communications Summit, 2007 Jul 1–5, Budapest, Hungary. Washington DC: IEEE, 2007. 1–5
Weingart T, Sicker D C Grunwald D. A statistical method for reconfiguration of cognitive radios. IEEE Wirel Commun, 2007, 14: 34–40
Weingart T, Sicker D C Grunwald D. A method for dynamic configuration of a cognitive radio. In: Proceedings of the 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks, 2006 Sept 25, Reston, VA, USA. Washington DC: IEEE, 2006. 93–100
Weingart T, Gary V Y Douglas C, et al. Implementation of a reconfiguration algorithm for cognitive radio. In: Proceedings of the 2nd Cognitive Radio Oriented Wireless Networks and Communications, 2007 Aug 1–3, Orlando, FL, USA. Washington DC: IEEE, 2007. 171–180
Song Z, Shen B, Zhou Z, et al. Improved ant routing algorithm in cognitive radio networks. In: Proceedings of the 9th International Symposium on Communications and Information Technology, 2006 Sept 28–30, Icheon, Korea. Washington DC: IEEE, 2009. 110–114
Zhao N, Li S, Wu Z. Cognitive radio engine design based on ant colony optimization. Wireless Pers Commun, 2011, 2011: 1–10
Andreotti R, Stupia I, Giannetti F, et al. Resource allocation in OFDMA underlay cognitive radio systems based on ant colony optimization. In: Proceedings of the IEEE Eleventh International Workshop on Signal Processing Advances in Wireless Communications, 2010 Jun 20–23, Marrakech, Morocco. Washington DC: IEEE, 2010. 1–5
Huang W, Chen J, Li S. A channel allocation algorithm for minimizing handoff rate in cognitive radio networks. In: Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008 Oct 12–14, Dalian, China. Washington DC: IEEE, 2008. 1–4
Doerr C, Sicker D C Grunwald D. Dynamic control channel assignment in cognitive radio networks using swarm intelligence. In: Proceedings of the 2008 IEEE Global Telecommunications Conference, 2008 Nov 30–Dec 4, New Orleans, LA, USA. Washington DC: IEEE, 2008. 1–6
Baldo N, Zorzi M. Learning and adaptation in cognitive radios using neural networks. In: Proceedings of the 5th IEEE Consumer Communications and Networking Conference, 2008 Jan 10–12, Las Vegas, NV, USA. Washington DC: IEEE, 2008. 998–1003
Adamopoulou E, Demestichas K, Theologou M. Enhanced estimation of configuration capabilities in cognitive radio. IEEE Commun Mag, 2008, 46: 56–63
Çalhan A, Çeken C. An adaptive neuro-fuzzy based vertical handoff decision algorithm for wireless heterogeneous networks. In: Proceedings of the IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications, 2010 Sept 26–30, Istanbul, Turkey. Washington DC: IEEE, 2010. 2271–2276
Hiremath S, Patra S K. Transmission rate prediction for cognitive radio using adaptive neural fuzzy inference system. In: Proceedings of the International Conference on Industrial and Information Systems, 2010 Jul 29–Aug. 1, Karnataka, India. Washington DC: IEEE, 2010. 92–97
Michael N. Artificial Intelligence: A Guide to Intelligent Systems. 2nd ed. London: Pearson Education, 2005
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Zhang, P., He, Q., Feng, Z. et al. Reconfiguration decision making in cognitive wireless network. Chin. Sci. Bull. 57, 3713–3722 (2012). https://doi.org/10.1007/s11434-012-5255-3
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DOI: https://doi.org/10.1007/s11434-012-5255-3