Cognitive Radio Spectrum Allocation And Decision Engine Based On The TLBO Algorithm

Cognitive radio technology is the most effective technology to solve the spectrum scarcity,its core technology including the spectrum allocation technology and cognitive decision engine technology.Spectrum allocation technology when the maximum benefit of the whole network,cognitive users in a distribution criterion obtained for an optimal spectrum allocation scheme;the cognitive radio system adjust objective of parameter according to the different working environment and the user requirement.At present,the spectrum allocation technology and cognitive decision engine technology have many scholars put a lot of effort to study and achieved good results,but along with the social spectrum resource becomes more and more scarce,its performance has the very big promotion space.The subject summarize and analysis various kinds of cognitive radio spectrum allocation model and decision engine model,for the existing problems in the model improve algorithms are proposed by this paper.Firstly in order to avoid the defect that spectrum allocation algorithm based on graph coloring theory model is low accuracy and low convergence speed.the paper presents the spectrum allocation algorithm based on the Binary Improve Teaching-Learning-Based Optimization algorithm,with the algorithm of good stability,the local search ability,in the average system efficiency criterion.The system not only improves the network efficiency,but also can make the cognitive users get the maximum benefits of spectrum resources.The simulation results show that,the proposed algorithm has faster convergence speed,better stability and users can obtain higher benefits system,especially in the large number of mass spectrum number and cognitive user optimization effect is more obvious.Secondly,according to the problem of transmitter parameter' s adjustment in cognitive radio system.This topic will be the Binary Improve Teaching-Learning-Based Optimization algorithm,is applied to the radio decision engine.Cognitive radio decision engine based on the Binary Teaching-Learning-Based Optimization algorithm,which less parameters,good ability of finding the best and strong capability in convergence is proposed in paper,so that the transmitter parameter according to the different working environment and the user requirement is adjusted.The simulation results,the proposed algorithm can quickly convergeto the global optimal solution and improve the convergence precision,cognitive radio decision engine can adjust the transmit power and modulation order work in different modes of communication,which can greatly satisfy the demand of the system,so that the algorithm solves cognitive radio system transmitter parameters problem.