COOPERATIVE SPECTRUM SENSING SCHEME USING FUZZY LOGIC TECHNIQUE

Rohit Kantikar, R. Yelalwar
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Abstract

In communication system spectrum has a crucial role and wireless technologies are increasing rapidly it is required to make efficient use of spectrum to satisfy the spectrum scarcity problem. Using spectrum efficiently can be done by cognitive radio because of its ability to sense surrounding environment. Cognitive radio sense unoccupied spectrum by detecting the primary users’ presence or absence in the spectrum. Using more than one cognitive radio in the detection process will increase the efficiency of spectrum usage and prevent interference between signals. Using machine learning techniques for the implementation of intelligent cognitive radio increase efficiency and detection performance and detect signal at low SNR condition. Fuzzy logic machine learning technique is implemented which is based on fuzzy membership functions and fusion centre. Energy detection is used to classify signal and noise at each cognitive radio after that each cognitive radio output information convert into membership function and apply fuzzy rules such as algebraic sum, the algebraic product give a final decision about the signal presence or absence. Simulation results show that the proposed system gives much better results compared to the conventional energy detection system and improves the performance of the system.
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基于模糊逻辑技术的协同频谱感知方案
在通信系统中,频谱起着至关重要的作用,随着无线技术的迅速发展,需要有效利用频谱来满足频谱稀缺的问题。认知无线电可以有效地使用频谱,因为它能够感知周围环境。认知无线电通过检测主要用户在频谱中的存在或不存在来感知未被占用的频谱。在检测过程中使用多个认知无线电将提高频谱使用效率,并防止信号之间的干扰。使用机器学习技术实现智能认知无线电可以提高效率和检测性能,并在低信噪比条件下检测信号。实现了基于模糊隶属函数和融合中心的模糊逻辑机器学习技术。能量检测用于对每个认知无线电的信号和噪声进行分类,然后将每个认知无线电输出信息转换为隶属函数,并应用代数和等模糊规则,代数乘积给出信号存在与否的最终决定。仿真结果表明,与传统的能量检测系统相比,所提出的系统给出了更好的结果,并提高了系统的性能。
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