Research On Strategy In Dynamic Spectrum Allocation Based On Cognitive Radio

The rapid development of modern wireless communication technology,especially the increasing application of the wireless multimedia technology,has made radio spectrum resource become more and more scarce.However,research has shown that the lack of spectrum resource is due to the inefficiency of the existing fixed spectrum management strategy.Therefore,a kind of intelligent communication system based on cognition is proposed by academia.How to make cognitive radio systems sense environment,learn from environment by artificial intelligence so as to achieve flexible and reliable communication meeting the requirements of subscribers in complex communication systems and efficient utilization of spectrum has ever been one important research topic of modern wireless communication sytems,and also been the objective of this dissertation.This dissertation mainly focuses on the dynamic spectrum allocation based on cognitive radio.Firstly,the paper will introduce the cognitive radio technology ,including its definition and key technologies.Then the paper will describe several important models of the spectrum allocation technology.As the existing algorithms can't allocate the spectrum in an autonomously deciding mode in heterogeneous radio networks,a new centralized algorithm based on reinforce learning is proposed in this paper.Simulation results show that the proposed algorithm can get better spectrum efficiency and quality of service with respect to fixed frequency planning scheme and general dynamic spectrum allocation policy.Aiming at the bottleneck of centralized strategy in bandwidth and complexity,two improved distributed stategies based on reinforce learning are proposed which are applied to heterogeneous multicast communication systems.Simulation results show the good performance of the strategies.