Research On Key Techniques Of Cognitive Radio Based On Quantum-inspired Swarm Intelligence

Cognitive radio(CR)is widely considered to be one of the most promising technologiesin the future wireless communications,which aims to realize the efficient utilization of spectrum resources.The maincomponents of the CRS are the intelligent management systemand reconfigurable radios.Cognitive radio system senses cognitive radio environmentwith cognitive ability,and adjusts the operational parameters and protocols of its reconfigurableradios to satisfy the system requirements.The key technologiesare closely related with cognitive radioincluding cognitive decision engine technology and the spectrum allocation technology.The two technologies are also associatedwith the performance of the whole cognitive radio system and spectrum utilization,and are alsothe problems which need to break through in order to realize the transformationfrom theoretical research into industry for cognitive radio technology.Decision engine and spectrum allocation of cognitive radio areregarded as discrete combination optimization problems,and can be solvedby intelligent optimization algorithms,such as swarm intelligence algorithm.But there are someproblems of convergence speed and convergence accuracy in the optimization process for some key cognitive radio technologies.How to design a new algorithm to solve them is a valuable research direction.This paper focus on the design of quantum swarm intelligent algorithms,and its application in the cognitive decision engine and spectrum allocation problems.The main content of this paper can be summarized as the following aspects:(1)In order to improve the performance of cognitive decision engine parameter adjustment,appropriate mathematical models are estabished,and design three quantum swarm intelligent algorithms to implement the design of cognitive decision engine.Firstly,the proposed cognitive decision engine based on membrane-inspiredquantum bee colony optimization(MQBCO)can overcome the limitation of current research,and can obtain better convergence performance.Secondly,the proposed cognitive decision engine based on the hybrid membrane-inspired quantum goose algorithmemploying the hybrid codingcan overcome the shortcomingofquantization error caused by discrete coding in the existing researches,andcan obtain accurated system parameters.Lastly,the multi-objective cognitivedecision engine based on multi-objective quantum ant colony algorithm is proposed,in which a multi-objective optimization model is established andnon-dominated sorting and crowded degreecomputation are introduced into the algorithm,thus we can getPareto solutions.The proposed method can overcome the shortcomings that transform the muti-objective optimization problem into single objective optimization problem by the simple weighted methodand do not consider the requirements of the multiple indicators of communication performance in the existing researches.And the proposed method can achieve good convergence performance and meet the real-time requirements.(2)The problem of cognitive radio spectrum allocation is treated as a single objective optimization problem to deal within the current research,which cannotmaximize network efficiency and fairness at the same time.So this paper firstly establishes the mathematical model of multi-objective spectrum allocation,and then design muti-objective membrane-inspiredquantum bee colony optimizationbased on non-dominated solution sorting the solutions according to the basic principle of multi-objective evolution algorithms,and studies how to solve the multi-objective spectrum allocation problem with it.The proposed method takes care of network efficiency and fairness between users at the same time.By introducing non-dominated solution sorting and crowded degree computation,the Pareto front solution set can be obtained.The cognitive radio system can selectthe resonable weights according to the specific requirements of users,and get reasonable spectrum allocation scheme that could satisfy the system requirements,and this method can also solve the single objective spectrum allocation optimization problem.(3)A mathematical model for parameter adjustment problem in the green cognitive radio is presented,and it is evolved from the basis of cognitive decision engine while consideringthe constraintsof environment.Then according to the principle of quantum computing and bacteria foraging,a quantum bacterial foraging algorithm which can solve continuous optimization problems and discrete optimization problems is proposed,and its convergence analysis is given.To demonstrate the superiority of the proposed quantum bacterial foraging algorithm,some benchmark functionsare used to test and analyze the performance of the proposed algorithm.Apply the quantum bacterial foraging algorithmtosolve parameteradjustment problem in green cognitive radio,the simulation results show that in comparation with the previous researches,the proposed method can obtain higher objective function value,i.e.it can adjust parameters of cognitive radio system adaptively,satisfy the quality of service(QoS)requirements of the target user,and realize communication with low consumptionin the safe and reliable manner.