Research On Decision-making Mechanism For Improving Adaptability Of Cognitive Radio

With the continuous the growth of services demand and development of wireless communication technology,the contradiction between the scarcity of spectrum resources and the low utilization of spectrum under the static spectrum allocation mechanism has become increasingly prominent.Especially in order to meet the requirement of wireless communication in the information society after 2020,5G broadband wireless communication system will become a heterogeneous network system with multi-service and multi-technology integration.It has the characteristics of wide-area,ultra-intensive,high dynamic and complex heterogeneity.It can not meet the development needs of 5G communication technology only through traditional static resource allocation.Cognitive radio technology is a technology that can effectively improve spectrum utilization.However,the improvement of adaptability of cognitive radio to external environment and internal needs is highly required for following reasons: the complexity of wireless communication environment,the variability of secondary user application scenarios,the uniqueness of the relationship between primary and secondary users in spectrum usage,as well as the demand of 5G network interconnection and multi-network convergence.The adaptability of cognitive radio refers to the ability of cognitive radio system to adapt optimally according to internal and external changes,which is mainly embodied in the decision-making mechanism.To improve the adaptability of cognitive radio,it is necessary to improve the flexibility of decision-making mechanism fundamentally so as to select decision-making modes flexibly according to the changing decision-making conditions and achieve the purpose of improving decision-making effect.This paper designs a decision-making algorithm of cognitive radio system that takes into account both effectiveness and flexibility,and improves the adaptability of cognitive radio system from the aspects of spectrum acquisition mode,spectrum access strategy,resource allocation scheme and so on,so as to achieve the goal of improving spectrum utilization.In this paper,based on the two system framework of cognitive radio system,we have difference objectives.Centralized cognitive radio system is easier for convenient control and decision making,so the decision-making mechanism of centralized cognitive radio system provides more flexibility.Distributed cognitive radio system is more flexible and autononous,so the decision-making mechanism of distributed cognitive radio system provices better performance of global decisions.The main contents of the research include the following aspects:(1)The centralized cognitive radio system,because of its central controller,can achieve efficient and controllable decision-making effect in the whole secondary user system and has a certain degree of effectiveness.However,decision-making objectives in centralized cognitive radio systems are often designed for the whole system,lacking consideration for the change of individual users' needs.In decision-making algorithms,they are usually designed only according to the specific needs of specific communication scenarios,and lack of discussion and Research on the flexibility and generality of the algorithm,which can not fully guarantee the adaptability requirements of cognitive radio in dynamic environment and demand.To solve this problem,we design the decision-making algorithm from two aspects: spectrum access and resource allocation.The adaptability of cognitive radio system under dynamic environment and demand is guaranteed from the effectiveness and flexibility of the algorithm,so as to further improve the spectrum utilization.In the aspect of spectrum access,a multi-strategy dynamic spectrum access system is designed for the requirement of flexibility of decision-making mechanism in cognitive radio networks.In this multi-strategy dynamic spectrum access system,primary users and secondary users share and utilize the spectrum through multiple strategies at the same time.We modeled the system by continuous-time Markov chain(CTMC)and obtained the mathematical expression of the parameters related to the performance of spectrum access.On this basis,two optimization schemes of spectrum unit allocation and false alarm probability selection are proposed.Finally,numerical simulation verifies that our proposed MS-DSA system can flexibly adjust the access strategy according to the demand of secondary users and primary users for the current spectrum resources and improve the adaptability of the system.In the aspect of resource allocation,an adaptive resource allocation algorithm with flexibility and generality is designed for the flexibility of decision-making mechanism in cognitive radio networks under dynamic environment and demand.In this paper,the problem of resource allocation in centralized cognitive radio communication system is abstracted as a mathematical model to solve the optimization problem.In order to guarantee the flexibility of the adaptive resource allocation algorithm,we introduce the Particle Swarm Optimization(PSO),which has a general optimization process,and propose an improved PSO to overcome the shortcomings of PSO.This algorithm introduces Crossover and Mutation mechanism in the process of particle updating,which increases the diversity of particles and improves the global optimization ability of the algorithm.While retaining the advantages of the original particle swarm optimization algorithm,it also improves the effectiveness of the allocation algorithm while guaranteeing flexibility.Then three different application scenarios are analyzed,one adaptive resource allocation algorithm can applied in different scenarios and different services.The flexibility of the decision algorithm is verified,and the adaptability of the resource allocation mode of cognitive radio system is effectively improved.(2)In the distributed cognitive radio system,the system architecture itself has sufficient flexibility.This flexibility comes from the secondary users,the lack of central control,and every secondary user to make decisions according to their own needs.Secondary users from different secondary user systems want to maximize their throughput at a low cost,which makes most of the decision-making methods used in the centralized cognitive radio system cannot apply in distributed cognitive.Dealing with this objective problem in distributed cognitive radio,we propose a secondary user collaboration framework based on evolutionary game theory.Evolutionary game theory provides an excellent means to address the strategic uncertainty that a user/player may face by exploring different actions,adaptively learning during the strategic interactions,and approaching the best response strategy under changing conditions and environments using replicator dynamics.We derive the dynamic behavior of secondary users and the evolutionary stable strategy(ESS),and prove that the dynamic behavior of secondary users converges to ESS,which makes it possible to use this spectrum sensing game model in a distributed cognitive radio system.According to the dynamic behavior of this secondary user,we propose a learning method for the distributed cognitive radio,in which the secondary users can approach the ESS solely based on their own payoff observations.Simulation results show that the average throughput achieved in the proposed cooperative sensing game is higher than the case where secondary users sense the primary user individually without cooperation.The proposed game is demonstrated to converge to the ESS,and achieve a higher system throughput than the fully cooperative scenario,where all users contribute to sensing in every time slot.Secondary users choose strategy adaptively according to distributed learning method,which effectively improves the adaptability of the system.