摘要
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.
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 match- er. 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 ex- traction,rule generation for CRN cross-layer learning. The proposed model can be validly used in knowledge learning for CRNs.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2012年第1期155-161,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.61072138)
国防基础科研计划(No.B3120110005)
国家973重点基础研究发展计划(No.2009CB320403)
西安电子科技大学ISN实验室开放课题(No.ISN10-09)
关键词
认知网络
规则生成
学习引擎
跨层设计
cognitive radio networks
rule generation
learning engine
cross-layer design
