基于多属性的二次迭代SAW供需匹配网络的研究

由于目前密集的无线通信用户数量、海量的通信业务数据交互的发展趋势,要求通信网络在单位面积上提供更优的供需匹配平衡策略.现有的无线网络频谱资源分配是一种固定分配方式,无法高效地利用有限的频谱资源,使得供需频谱分配不平衡性凸显.针对认知无线电技术领域中频谱资源管理进行了研究,提出一种基于多属性的二次迭代SAW供需匹配网络选择方法.该算法将频谱切换过程中采用的频谱判决机制由传统的顺序匹配法转为多属性加权判决.通过融合多属性效用理论和将无线通信业务分级,优化决策效果,改善了顺序匹配法,大大提高了频谱利用率.通过仿真对比,可以看到在同一段频谱的分配增益牺牲较小的应用场景下,频谱切换开销得到了大幅度降低,较理想地简化了分配方案的复杂度,进而提高了频谱利用率,使得算法的优化得到具体实现.

The research about the matching network of supply and demand in the second iteration SAW based on multi-attribute

The current number of intensive-users have been increasing dramatically and interactive data require a better balance of supply-demand policy in communications network.Currently,fixed spectrum slices are licensed to each wireless service,as a result,a large portion of the licensed spectrum is underutilized.In this paper,we research spectrum resource management in the field of cognitive radio technology and propose a method of matching network of supply and demand in the second iteration SAW based on multi-attribute.It changes the spectrum decision mechanism from the traditional matching method to multiple attribute weighted judgment method in the switch,base on multi-attribute utility theory.Through the fusion of multiple attribute utility theory and wireless communications business classification,it optimizes decision-making effect,improvs the matching order method and enhances the spectrum utilization greatly.Above new method were demonstrated by simulation,the effect of improvement is good.By comparing the simulation can be seen in the same period of the spectral distribution of the gain at the expense of smaller application scenarios,the spectrum switching overhead has been greatly reduced,simplify the complexity of the distribution plan,thereby increasing the spectrum efficiency.Thus optimization algorithm has been making specific implementation.