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A combination of double sided neighbor distance and Genetic Algorithm in cooperative spectrum sensing against malicious users

机译:双边邻域距离和遗传算法相结合的协同频谱感知恶意用户

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In Cognitive Radio Network (CRN) secondary users (unlicensed users) try to access radio spectrum of the primary users (licensed user) if not utilized by the licensed user. Cooperative spectrum sensing (CSS) is one way to acquire accurate status of primary user for achieving maximum utilization of the vacant spectrum. Existence of malicious users in CSS can drastically reduce the performance of the system. In this paper, we study how to minimize the effect of false spectrum sensing data coming from abnormal secondary users. The proposed work focuses on the use of double sided neighbour distance (DSND) algorithm along-with Genetic Algorithm (GA) for the detection and avoidance of misbehaving users in CSS. The scheme is tested against the existence of opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU) and always no malicious user (ANMU) sending spectrum sensing data to the fusion centre (FC) with normal secondary users (SUs). Simulation result shows effectiveness of the proposed method in making the results of the majority voting hard fusion combination scheme more accurate and reliable against simple hard fusion schemes and equal gain combination (EGC) in the presence of malicious users in CSS.
机译:在认知无线电网络(CRN)中,如果未由许可用户使用,则次要用户(非许可用户)会尝试访问主要用户(许可用户)的无线电频谱。合作频谱感测(CSS)是获取主要用户准确身份的一种方式,以实现对空频谱的最大利用。 CSS中存在恶意用户会大大降低系统性能。在本文中,我们研究如何最小化来自异常二级用户的虚假频谱感测数据的影响。拟议的工作集中在使用双面邻居距离(DSND)算法以及遗传算法(GA)来检测和避免CSS中行为不当的用户。该方案针对是否存在反向恶意用户(OMU),随机反向反向恶意用户(ROMU),始终是恶意用户(AYMU)以及始终没有恶意用户(ANMU)将频谱感测数据发送到融合中心(FC)的情况进行了测试普通二级用户(SU)。仿真结果表明,在CSS中存在恶意用户的情况下,该方法对于多数投票硬融合组合方案的结果,相对于简单的硬融合方案和等增益组合(EGC)而言,更加准确可靠。

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