Research On Spectrum Resource Management Strategy Of Cognitive Radio Network

With the rapid development of the Internet of Things,more and more intelligent terminal devices are connected to the network and the demand for spectrum resource has risen sharply.However,due to the limited spectrum resource and the unreasonable spectrum management strategy,spectrum scarcity has increasingly become serious.As an intelligent adaptive network,Cognitive Radio Network(CRN)enables the Secondary Users(SU)to dynamically access the Primary Users(PU)licensed spectrum through Cognitive Radio(CR)technology,effectively solving the shortage of spectrum resource.Therefore,aiming at the problem of insufficient spectrum resource utilization,this thesis investigates the spectrum resource management strategy in Cognitive Radio Network.Then,we design allocation strategies under different spectrum sharing modes based on auction theory and swarm intelligence evolution theory.The specific research contents are as follows:Firstly,research on the spectrum allocation strategy of Cognitive Radio Network in Overlay mode.In the Overlay spectrum sharing mode,auction has been proved to be one of the efficient solutions.However,most of the spectrum auction models in the existing research are only suitable for large-scale network users with sufficient budget.Secondary Users in small networks with limited budget cannot become auction winners in such auction mechanisms to obtain benefits.On the basis of this consideration,this thesis constructs a spectrum allocation model based on Group-Buying strategy with spectrum reuse mechanism.In this model,Secondary Users with insufficient budget will firstly form the buying groups according to the designed budget extraction algorithm of random sampling groups.Meanwhile,they submit group budget to their proxy buyers.Then the proxy buyers submit a bid to the Primary Users.The Primary Users will group the proxy buyers according to the user grouping algorithm on the basis of interference avoidance to achieve spectrum reuse.Finally,the entire auction process is completed rely on the matching pricing algorithm.The simulation results show that Secondary Users with insufficient budget can also obtain the spectrum by the aid of the designed spectrum allocation strategy in this thesis.At the same time,compared with TASG,TRUBA and TRUBA~+,it effectively enhances the utility and spectrum utilization of the system.Secondly,research on the spectrum allocation strategy of Cognitive Radio Network in Underlay mode.Aiming at the problem that the existing allocation algorithms in Underlay mode do not take the fairness of user rate proportion into account or have the low fairness,this thesis constructs a spectrum allocation model based on rate proportion constraint.We abstract it as a joint optimization problem of sub-channel allocation and power control.Then two allocation algorithms are designed based on swarm intelligence evolution theory.One is to divide the joint optimization problem into independent sub-problems to solve.A two-stage spectrum allocation algorithm based on Artificial Bee Colony(ABC)algorithm is designed.The other regards the problem as an entirety and design a joint sub-channel allocation and power control algorithm based on Improved Elite Selection Genetic Algorithm(IEGA).The simulation results show that the two algorithms fully consider the interference power limitation of Underlay mode in this thesis.Moreover,they enhance the rate proportional fairness between Secondary Users while maximizing the system throughput of the Cognitive Radio Network.