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A fuzzy neural approach for dynamic spectrum allocation in cognitive radio networks

机译:认知无线电网络动态频谱分配模糊神经方法

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In this paper, decision making scheme in cognitive radio is proposed by using fuzzy neural system, due to which secondary users can utilizes the spectrum effectively with seamless communication between cognitive radio and primary users. The spectrum sensing performance is enhanced by using either multiple antennas or multistage spectrum sensing. Due to multiple antennas at sensing node causes more equipment cost in spectrum sensing technique, therefore two stage spectrum sensing scheme is introduced. The proposed fuzzy neural decision making technique include two stage spectrum sensing schemes for identifying available spectrum. In first stage, three parameters such as spectrum utilization efficiency, degree of mobility and distance to the primary user of cognitive radio network are considered as inputs to fuzzy logic decision making process, while output of that process gives spectrum access decision, based on linguistic knowledge of 27 rules. Feedback neural network configuration included in second stage of spectrum sensing, which is trained with the help of generalized delta learning rule. Neural network has two input parameters such as output of fuzzy logic based spectrum sensing and desired values. The reference signal of neural network is obtain from transformation of output membership function in first stage to their mid singleton values. Simulation results shows significant improvement in sensing accuracy by exhibiting higher probability of detection.
机译:在本文中,通过使用模糊神经系统提出了认知无线电的决策方案,因为哪些二级用户可以在认知无线电和主要用户之间有效地利用频谱。通过使用多个天线或多级频谱感测来增强光谱感测性能。由于传感节点处的多个天线导致频谱感测技术中的更多设备成本,因此引入了两个级谱传感方案。所提出的模糊神经决策技术包括用于识别可用频谱的两个阶段光谱传感方案。在第一阶段,三个参数如频谱利用效率,移动性无线电网络主用户的迁移率和距离被认为是模糊逻辑决策过程的输入,而该过程的输出基于语言知识提供频谱访问决策27规则。反馈内部网络配置包括在频谱感测的第二阶段,这是借助广义增量学习规则训练的。神经网络具有两个输入参数,例如基于模糊逻辑的频谱感测和所需值的输出。神经网络的参考信号从第一阶段的输出隶属函数的转换中获得到它们的中间单例值。通过表现出更高的检测概率,仿真结果表明了感测精度的显着改善。

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