Research On Spectrum Handoff And Cognitive Decision Engine In Cognitive Radio

Cognitive radio technology is an intelligent wireless communication technology that can realize spectrum sharing and greatly improve spectrum utilization.It breaks the traditional way of static use of spectrum authorized by the government and achieves dynamic spectrum access.Dynamic spectrum access needs to solve a series of problems.This paper deeply studies the target channel selection,spectrum handoff and cognitive decision engine of cognitive radio.In terms of target channel selection mechanism,three target channel selection algorithms based on joint optimization of handoff delay and effective channel capacity are proposed.Firstly,related theory of multi-objective optimization is introduced,then three multi-objective optimization algorithms are deeply studied,which are multi-objective particle swarm optimization algorithm,non-dominated sorting genetic algorithm and pareto envelope-based selection algorithm.The encoding method,discrete position updating formula and crossover mutation strategy are improved.Finally,the handoff failure probability,effective channel capacity and cumulative handoff delay formula are deduced in detail in combination with the target channel access scenario in spectrum handoff.Three target channel selection are obtained by applying the three multi-objective optimization algorithms mentioned above.The simulation results show that all three methods can balance the real-time performance and high throughput of the network,multi-objective particle swarm optimization algorithm has the best handoff performance and the highest complexity;pareto envelope-based selection algorithm has the lowest complexity and the worst handoff performance,and non-dominated sorting genetic algorithm is between them.In terms of spectrum handoff of cognitive radio users,two algorithms are proposed.Firstly,a hierarchical local control spectrum handoff algorithm(HLC)is proposed.Nodes in the network will be divided into superior nodes and common nodes.The proposed spectrum handoff algorithm includes spectrum sensing,neighbor discovery,node grading,voting and handoff reconstruction,etc.However,this algorithm has the disadvantage of long handoff time due to the large number of superior nodes.Then,an improved hierarchical local control spectrum handoff algorithm(IHLC),which improves the steps of node grading,voting and handoff reconfiguration,and refers to neighbor node information within one hop and two hops,and introduces sub-advanced node.The simulation results show that both algorithms can achieve multiple pairs of cognitive radio users'handoff,and the IHLC algorithm has fewer advanced node numbers and lower handoff delays than HLC algorithm,while ensuring a smaller network interruption rate.In terms of cognitive decision engine,eight algorithms of cognitive decision engine are proposed.Firstly,two improved particle swarm optimization algorithms,IPSO and APSO,are proposed.In the IPSO algorithm,the learning factor of the particle swarm optimization algorithm is improved,and the perturbation term is added to select a more suitable transformation function.In the APSO algorithm,the adaptive inertia weight mechanism is introduced to the particle swarm optimization algorithm.Then two improved differential evolution algorithms,IDE algorithm and IBLDE algorithm,are proposed.In IDE,the fixed crossover probability of improved differential evolution algorithm(DE)is modified to adaptive crossover probability.In IBLDE algorithm,adaptive mutation mechanism is introduced into learning differential evolution algorithm.Secondly,two optimization structures are proposed:serial optimization and parallel optimization.Eight improved intelligent optimization algorithms are obtained by pairwise combination.Then eight algorithms are applied to the physical layer cognitive decision engine model to explore the parameters reconfiguration performance of the eight algorithms.Finally,eight algorithms are applied to the cross-layer cognitive decision engine model to explore the parameters allocation capability of the eight algorithms in cross-layer optimization.