基于粗糙集的认知无线网络跨层学习

摘要

Cognitive learning is a very important part for cross-layer design in cognitive radio networks (CRNs) .CRNs are required to take advantage of the known cross-layer parameters for learning environment and reconfiguring the network. This paper proposes a cross-layer learning scheme for CRN based on rough set,builds database of case events, knowledge base and rule matcher. This model solves the cross-layer learning in CRNs through combining data discretization, attribute reduction, value reduction and rule generation. By comparing the simulation results of typical testing data sets, a group of rough set algorithms are selected for the proposed model. The simulation results show that the set of algorithms can effectively solve accuracy and validity of knowledge extraction,rule generation for CRN cross-layer learning.The proposed model can be validly used in knowledge learning for CRNs.%认知学习是认知无线网络(CRN)跨层设计中非常重要的一环,它要求通信网络能利用已知跨层环境参数进行知识提取学习,并根据需要重配置网络.本文提出了一种基于粗糙集的CRN跨层学习技术,构建了案例事件库、知识库与规则匹配器,该模型结合数据离散、属性约简、值约简与规则生成算法来解决CRN的跨层学习问题.通过典型测试数据集的仿真比较,选出一组适合于所提出模型的粗糙集算法集合.仿真结果表明,该算法集能有效解决CRN跨层学习中知识提取与规则生成的准确性及有效性等问题,提出的跨层学习模型能有效用于CRN中的知识学习.