Membrane quantum particle swarm optimisation for cognitive radio spectrum allocation

To design a novel intelligence algorithm for spectrum allocation problem, a membrane quantum particle swarm optimisation (MQPSO) is proposed. The proposed MQPSO algorithm applies the theory of membrane computing to quantum particle swarm optimisation (QPSO), which is an effective discrete optimisation algorithm. Then the proposed MQPSO algorithm is used to solve spectrum allocation problems of cognitive radio system. By hybridising the QPSO and membrane theory, the quantum state and measure state of the quantum particle can be well evolved in membrane structure. The new spectrum allocation algorithm can search global optimal solution. Simulation results for cognitive radio system are provided to show that the designed spectrum allocation method is superior to some previous spectrum allocation algorithms.

[1]  Gheorghe Paun,et al.  A guide to membrane computing , 2002, Theor. Comput. Sci..

[2]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[3]  Michael N. Vrahatis,et al.  Tackling magnetoencephalography with particle swarm optimization , 2009, Int. J. Bio Inspired Comput..

[4]  Zhen Peng,et al.  Cognitive radio spectrum allocation using evolutionary algorithms , 2009, IEEE Transactions on Wireless Communications.

[5]  Zhao Zhijin,et al.  Cognitive radio spectrum assignment based on quantum genetic algorithm , 2009 .

[6]  Michael L. Honig,et al.  Auction-Based Spectrum Sharing , 2006, Mob. Networks Appl..

[7]  Chu Kiong Loo,et al.  Hybrid particle swarm optimization algorithm with fine tuning operators , 2009, Int. J. Bio Inspired Comput..

[8]  Haitao Zheng,et al.  Distributed spectrum allocation via local bargaining , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[9]  Gheorghe Paun Further Twenty Six Open Problems in Membrane Computing , 2005 .

[10]  Marian Gheorghe,et al.  A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem , 2008, Fundam. Informaticae.

[11]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[12]  Ajit Narayanan,et al.  Quantum-inspired genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Qiu Chen,et al.  Particle swarm optimisation algorithm with forgetting character , 2010, Int. J. Bio Inspired Comput..

[15]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[16]  Haitao Zheng,et al.  Collaboration and fairness in opportunistic spectrum access , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[17]  F. Jondral,et al.  Dynamic and local combined pricing, allocation and billing system with cognitive radios , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[18]  M. Diao,et al.  Quantum Particle Swarm Optimization for MC-CDMA Multiuser Detection , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[19]  Cristina Comaniciu,et al.  Adaptive channel allocation spectrum etiquette for cognitive radio networks , 2005 .

[20]  Ben Y. Zhao,et al.  Utilization and fairness in spectrum assignment for opportunistic spectrum access , 2006, Mob. Networks Appl..

[21]  Cristina Comaniciu,et al.  Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..