摘要
认知无线电是一种智能无线电通信系统，认知引擎的设计是其关键问题之一．认知引擎可通过推理与学习方法来实现认知无线电的感知、自适应与学习能力．为适合变化的无线环境，提出了用RBF神经网络设计认知无线电认知引擎的模型，根据经验知识和环境情况，动态重配置无线环境参数．基于802.11a仿真平台模拟认知无线电通信模型，经样本值训练神经网络，利用该网络学习信道特征，并建立基于RBF神经网络的认知引擎．在对感知信息学习后，实现根据无线环境和用户需求来配置通信参数的功能,仿真表明，这种认知引擎能有效地实现认知无线电的学习重构功能，其预测精度比BP网络模型高出了10％-30％．
Cognitive radio （CR） is an intelligent wireless communication system, and the design of a cognitive engine is one of the key issues. The cognitive engine can utilize the reasoning and learning methods to achieve the perception, adaptation and learning ability. This paper presents a design scheme for the cognitive engine based on the RBF neural network （RBF-NN） to adapt varied wireless environment according to experiential knowledge. A CR simulation cognitive engine is realized using the 802. 11 a simulation platform by collecting data and training the NN to learn channel characteristics. Under the premise of studying the sensation information, CR reconfiguration is realized according to the user＇s needs. Simulation results show that the proposed cognitive engine can effectively realize the study and reconfiguration function, and that the prediction accuracy is higher than that of the BP NN model by 10% - 30%.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2011年第1期159-164,170,共7页
Journal of Xidian University
基金
国家自然科学基金资助项目(61072138)
西安电子科技大学综合业务网理论及关键技术国家重点实验室资助项目(ISN10-09)
四川省教育厅基金资助项目(09ZA136)
关键词
认知无线电
认知引擎
调制
神经网络
cognitive radio
cognitive engine
modulation
neural networks
