Research On Key Techniques Of Green Cognitive Radio Based On Swarm Intelligence

Cognitive Radio(CR)has been widely used in dozens of fields such as satellite navigation,mobile communication system,traffic control and military communication.Because of its wide range of applications,since the day of its introduction,the various key technologies of cognitive radio have attracted people's great interest in research,which in recent years have rapid development.With the development of communication technology,the shortage of spectrum resources and the user's demand for quality of service(QoS)are becoming increasingly demanding,which also prompts people to constantly improve and explore new methods and techniques.Green cognitive radio is a new stage in the development of cognitive radio system.It can sense external wireless environment changes and intelligently adjust parameters to adapt to the change of environment while effectively utilizing energy.It has the potential to improve the performance of existing cognitive radio systems,and is expected to be applied in 5G and other future communication systems.Aimed at the theory and application problems of spectrum sensing,cognitive system parameter design and information and energy co-transmission in existing green cognitive radio systems,the paper designs a new swarm intelligent algorithm to solve complex engineering problems,and obtains the optimal solution of spectrum sensing,cognitive system parameter design and information and energy co-transmission in green cognitive radio systems to promote the development and application of cognitive radio.Therefore,aimed at the difficult problem in the green cognitive radio system,this paper designs efficient and reliable swarm intelligence algorithm to obtain the new methods of spectrum sensing,cognitive system parameter design and information and energy co-transmission technology,and promotes the development of green cognitive radio.The main contents of this paper can be described as following aspects:(1)In order to solve the problem that the convergence solution is not accurate and it can not solve the multi-objective joint optimization effectively,a culture bacterial foraging spectrum sensing method and a multi-objective quantum glowworm spectrum sensing method are proposed.The proposed spectrum sensing method basedon cultural bacterial foraging algorithm is a fast and accurate intelligent spectrum sensing method.Compared with the classical methods to solve spectrum sensing problem,the proposed spectrum sensing method has more superior performance.In order to solve the shortcoming of the existing spectrum sensing methods,which only consider the detection probability or false alarm probability,a multi-objective spectrum sensing model is proposed.The multi-objective spectrum sensing method based on multi-objective quantum glowworm swarm optimization is proposed,which comprehensively considers user's different demand of detection probability or false alarm probability,and obtains a satisfactory multi-objective solution set.(2)In order to solve the problem that the system parameters design of the green cognitive radio system is constrained by the convergence accuracy and convergence speed and the optimization of the multi-objective is conflicting,cognitive radio system parameter design methods based on quantum glowworm swarm optimization and quantum multi-objietive multi-species symbiotic evolution are proposed.The proposed green cognitive parameter design method based on quantum glowworm swarm optimization algorithm makes use of the quantum computing theory and the glowworm algorithm,which can improve the convergence ability of the algorithm,and consider the requirements of different communication indicators in the premise of low energy consumption.The paper is aimed at the shortage that existing cognitive system parameter design method transformed multi-objective optimization problem into single-objective optimization problem by simple linear weighting,which can not effectively consider the quality requirement of the user.Under the premise of ensuring reliable communication,the paper establishes a mathematical model of multi-objective green cognitive system parameter design,and designs quantum multi-objective multi-species symbiotic evolution algorithm.A multi-objective and multi-population symbiotic evolutionary algorithm is proposed,and the Pareto front-end solution set is obtained by non-dominated solution sort and crowded degree calculation.This method can ensure the reliability of the system,and at the same time,it can take into account multiple performance indicators and reduce energy consumption.(3)Aimed at the conflict of wireless energy harvesting and resource allocation inwireless energy harvesting green cognitive radio system,the paper establishes 3mathematical models of resource allocation and cooperative communication.The continuous optimization algorithm such as the quantum gray wolf optimization,the quantum bat algorithm,and the quantum fireworks algorithm are proposed to solve the technology problem,and further improve the performance of the system and expand the scope of application.Firstly,the proposed green wireless energy harvest and distribution method based on quantum gray wolf optimization algorithm,which can obtain the system's minimum energy loss,and achieve green communication and store energy simultaneously.Secondly,the proposed wireless energy harvest and cognitive cooperation strategy based on quantum bat optimization takes a more effective secondary user energy harvesting mode and information transmission mode,obtaining the optimal cooperation transmission program of energy and information at the same time,and the Secondary user system can harvest energy from the PU's signal and achieve self-supply.Finally,a quantum fireworks algorithm is proposed,and analyzed its convergence performance.The thesis proposes an optimal cooperative mechanism,which can consider PU's instantaneous transmission rate,PU's energy supply rate and maximizing throughput of PU and SU,and realize the coordinated transmission of information and energy.