电子学报2012，Vol.40Issue(1)：155-161,7.DOI:10.3969/j.issn.0372-2112.2012.01.025
基于粗糙集的认知无线网络跨层学习
Cross-Layer Leaming in Cognitive Radio Networks Based on Rough Set
摘要
Abstract
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.关键词
认知网络/规则生成/学习引擎/跨层设计Key words
cognitive radio networks/rule generation/ learning engine/ cross-layer design分类
信息技术与安全科学引用本文复制引用
江虹,伍春,包玉军,黄玉清..基于粗糙集的认知无线网络跨层学习[J].电子学报,2012,40(1):155-161,7.基金项目
国家自然科学基金(No.61072138) （No.61072138）
国防基础科研计划(No.B3120110005) （No.B3120110005）
国家973重点基础研究发展计划(No.209CB320403) （No.209CB320403）
西安电子科技大学ISN实验室开放课题(No.ISN10-09) （No.ISN10-09）
