关键词: 认知网络, 规则生成, 学习引擎, 跨层设计

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.

Key words: cognitive radio networks, rule generation, learning engine, cross-layer design