基于图论的认知无线网络频谱动态分配

Dynamic Spectrum Allocation in Cognitive Radio Networks Based on Graph Theory

在认知无线电网络中,图论与量子遗传算法相结合的频谱分配策略能够提高频谱利用率,但存在早熟和收敛精度不够等缺点。为了解决该问题而实现算法的优化,对图着色理论的频谱分配模型进行数学建模,并针对该模型提出了改进的量子遗传算法。首先,通过使用小生境技术初始化种群,使种群分布更加广泛、算法的收敛度更高;其次,根据进化代数对量子旋转角进行实时动态调整,对染色体进行阈值变异,防止个体陷入早熟,跳出局部解;然后,对干扰约束条件进行重新设计,有效地避免盲目性,提高了网络的公平性和网络效益。仿真结果表明,所提算法有效地提高了频谱利用率,极大地增强了网络系统的性能。

In cognitive radio network(CNR),for premature convergence and insufficient convergence precision in traditional algorithm,a spectrum allocation strategy combining graph theory with quantum genetic is recommended naturally to resolve bewilderment.In order to achieve optimal algorithm,the graph coloring spectrum allocation strategy is modeled in mathematics.First,in order to improve convergence,the quantum rotation angle algorithm is adjusted dynamically according to evolutionary algebra by using niche technology to initialize the population.Second,to prevent falling into precocity and jumping out of local solutions,Chromosomes are subjected to change for variable threshold.Third,for avoiding blindness the interference constraint is redesigned reasonably in order to improve the fairness and efficiency.Simulation results show that the improved quantum genetic algorithm can effectively improve the spectrum utilization and greatly enhance the performance of the network system.