Spectrum Allocation Strategy Based On Chaotic Binary Firefly Algorithm

With the rapid development of wireless communication industry,the number of end users is very large.The 5G system is envisaged to support the explosive growth of mobile data traffic demand and improve service quality at the same time.The demand of high data rate,low delay and increasing capacity require us to explore and get the utmost out of spectrum resource.Because the available spectrum resources are quite limited and the utilization rate is relatively low.The emergence of cognitive radio brings hope to the rational utilization of spectrum resources.Cognitive radio spectrum allocation technology takes leading position in the field of wireless communication technology with great advantages.In the environment of scarce spectrum resources,this technology has broad application prospects in the field of resource allocation.Since the result of spectrum allocation directly determines the performance and communication quality of the system,how to formulate spectrum allocation strategy is crucial.This thesis introduces the relevant knowledge of cognitive radio,including system composition,key technologies,allocation model,allocation method,and evaluation ind ex of spectrum allocation performance.It focuses on the spectrum allocation optimization of firefly algorithm in cognitive radio network,and comprehensively considers the system benefit and user fairness.(1)On the basis of the binary firefly algorithm,this thesis deeply studies the spectrum allocation optimization algorithm.Combined with the relevant theories of cognitive radio networks,the spectrum allocation problem is modeled with the help of graph theory coloring model.Aiming at the problem that the optimization effect of the current spectrum allocation optimization algorithm is not ideal,resulting in the low total benefit of the system,and the disadvantage that the search of binary firefly algorithm is easy to fall into local optimization,a binary firefly spectrum allocation strategy integrating logistic mapping is proposed.With the help of logistic chaotic mapping,the random moving step size and random number in the position update formula of firefly algorithm are optimized,and the results of the mapping are modified to make the algorithm jump out of the local optimum quickly;Binary conversion of the position of fireflies is carried out in an adaptive way to improve the exploration ability of the algorithm in the early stage of operation and the development ability at the end of operation.The simulation results show that the improved chaotic binary firefly can achieve more efficient spectrum allocation.(2)In this thesis,a spectrum allocation strategy based on brightness priority for the crazy firefly algorithm is proposed.The crazy operator is introduced to improve the diversity of the firefly population through the determined crazy probability,and the firefly brightness priority moving rule is formulated to guide the firefly to complete the iterative upd ate,improve the optimization quality of the algorithm,and promote the spectrum allocation optimization to get a better solution.At the same time,in view of the traditional spectrum allocation based on graph theory coloring model,the user fairness cannot be guaranteed,and there is unfair competition among users,a spectrum allocation benefit and fairness constraint mechanism is established,which can ensure the benefit and user fairness at the same time,achieve efficient and reasonable spectrum allocation on the whole,and improve the utilization of spectrum resources.The simulation results show that the spectrum allocation strategy of the crazy firefly algorithm based on brightness priority can effectively improve the proportional fairness of cognitive radio systems without affecting the overall benefits of the system.