Abstract
The recent increasing interest in cognitive radio networks has motivated the study and development of new approaches capable of co** with the intrinsic challenges of this kind of network, such as dynamic spectrum availability, distributed and heterogeneous network architectures, and soaring complexity. The bio-inspired approaches, with appealing characteristics such as autonomy, adaptation and collective intelligence of collaborative individuals, have been extensively studied. This paper presents a comprehensive survey of bio-inspired approaches for cognitive radio networks, emphasizing their domains of application. Specifically, ant colony optimization and particle warm optimization are further investigated with examples and numerical simulation.
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References
Mitola J III, Maguiren G Q. Cognitive radio: Making software Aug. radios more personal. IEEE Pers Commun, 1999, 6: 13–18
Dressler F, Akanb O B. A survey on bio-inspired networking. Comput Netw, 2010, 54: 881–900
Dressler F, Akanb O B. Bio-inspired networking: From theory to practice. IEEE Commun Mag, 2010, 48: 176–183
Dorigo M, Di Caro G. The Ant Colony Optimization Meta-heuristic, New Ideas in Optimization. London: McGraw Hill Press, 1999
Dorigo M, Di Caro G, Gambardella L M. Ant algorithms for discrete optimization. J Artif Life, 1999, 5: 137–172
Song Z, Shen B, Zhou Z, et al. Improved ant routing algorithm in cognitive radio networks. In: Proceedings of the 9th IEEE International Symposium on Communications and Information Technologies, 2009 Spet 28–30, Incheon. Washington DC: IEEE, 2009. 110–114
Bian K, Park J. Segment-based channel assignment in cognitive radio ad hoc networks. In: Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2007 Aug 1–3, Orlando. Washington DC: IEEE, 2007. 327–335
Salehinejad H, Talebi S, Pouladi F. A metaheuristic approach to spectrum assignment for opportunistic spectrum access. In: Proceedings of the 17th IEEE International Conference on Telecommunications, 2010 Apr 4–7, Doha. Washington DC: IEEE, 2010. 234–238
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 11th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Marrakech. 2010 Jun 20–23. Washington DC: IEEE, 2010. 1–5
Yu F R Huang M, Tang H. Biologically inspired consensus-based spectrum sensing in mobile ad hoc networks with cognitive radios. IEEE Netw, 2010, 24: 26–30
Lin R, Niu K, Xu W, et al. A two-level distributed sub-carrier allocation algorithm based on ant colony optimization in OFDMA systems. In: Proceedings of the 71st IEEE Vehicular Technology Conference, 2010 May 16–19, Taipei. Washington DC: IEEE, 2010. 1–5
Kennedy J, Eberhart R C Shi Y. Swarm Intelligence. San Francisco: Morgan Kaufman Publishers, 2001
Coello C A C Lamont G B Veldhuizen D A V. Evolutionary Algorithms for Solving Multi-objective Problems. 2nd ed. New York: Springer, 2007
Shi Y, Eberhart R C. Parameter selection in particle swarm optimization. In: Proceedings of the 7th Annual Conference on Evolutionary Programming. 1998 Mar 25–27, San Diego. London: Springer-Verlag, 1998. 591–600
Renk T, Kloeck C, Burgkhardt D, et al. Bio-inspired algorithms for dynamic resource allocation in cognitive wireless networks. In: Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2007 Aug 1–3, Orlando. Washington DC: IEEE, 2007. 431–441
Zheng S, Lou C, Yang X. Cooperative spectrum sensing using particle swarm optimization. IEEE Electron Lett, 2010, 46: 1525–1526
Zhang B, Hu K, Zhu Y. Spectrum allocation in cognitive radio networks using swarm intelligence. In: Proceedings of the 2nd International Conference on Communication Software and Networks, 2010 Feb 26–28, Singapore. Washington DC: IEEE, 2010. 8–12
Zhao Z, Peng Z, Zheng S, et al. Cognitive radio spectrum allocation using evolutionary algorithms. IEEE Transactions Wirel Commun, 2009, 8: 4421–4425
Atakan B, Akan O B. BIOlogically-inspired spectrum sharing in cognitive radio networks. In: Proceedings of the IEEE Wireless Communications and Networking Conference, 2007 Mar 11–15, Hongkong. Washington DC: IEEE, 2007. 43–48
Xu S, Zhang S, Lin W. PSO-based OFDM adaptive power and bit allocation for multiuser cognitive radio system. In: Proceedings of 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009 Sept 24–26, Bei**g. Washington DC: IEEE, 2010. 1–4
Derakhshan-Barjoei P, Dadashzadeh G, Razzazi F, et al. Bio-inspired distributed beamforming for cognitive radio networks in non-stationary environment. IEICE Electron, 2011, 86: 332–339
Letaief K B Zhang W. Cooperative communications for cognitive radio networks. Proc IEEE, 2009, 97: 878–893
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He, Z., Niu, K., Qiu, T. et al. A bio-inspired approach for cognitive radio networks. Chin. Sci. Bull. 57, 3723–3730 (2012). https://doi.org/10.1007/s11434-012-5216-x
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DOI: https://doi.org/10.1007/s11434-012-5216-x