Improvement in Co-Operative Spectrum Sensing Using ILP and GA in Cognitive Radio Network

Nisha Morasada, Ketki C. Pathak
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Abstract

Wireless technologies have grown at a speed and with the passing year, it shows clearly that their consumers are also increasing, that increment in wireless spectrum consumer increases the demand for spectrum. But for our wireless systems, the spectrum is divided into two categories: the first is licensed and the second is unlicensed. Licensed spectrum is used by authorized users and unlicensed spectrum is free for all users. But most of the time it is shown the hat licensed spectrum may not be properly utilized by primary users (PUs), at that time spectrum band is free. To mitigate that inappropriate use of spectrum cognitive radio (CR) network is used. In CR there is one challenge that among the CR node some nodes experience an impact of multipath and shadowing, and another is to sense the spectrum under a lower signal to noise ratio. To overcome the effect of multipath and shadowing co-operative spectrum sensing has been used but it has large energy utilization. This extra energy is consumed in sensing the spectrum and reporting each nodes local decision to Fusion Centre (FC). In this paper we discuss three different schemes for total sensing time and energy decrement or throughput improvement. Here we go after for the genetic algorithm and integer linear programming scheme for overall energy minimization and throughput maximization.
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基于ILP和遗传算法的认知无线网络协同频谱感知改进
无线技术的发展速度很快,随着时间的推移,它清楚地表明,他们的消费者也在增加,无线频谱消费者的增加增加了对频谱的需求。但对于我们的无线系统,频谱分为两类:第一类是授权的,第二类是未经授权的。授权频谱仅供授权用户使用,未授权频谱对所有用户免费。但大多数情况下,主要用户(pu)可能没有很好地利用许可频谱,此时频谱是空闲的。为了减轻频谱认知无线电(CR)网络的不当使用。在CR中,一个挑战是在CR节点之间的一些节点受到多径和阴影的影响,另一个挑战是在较低的信噪比下感知频谱。为了克服多径和阴影的影响,采用了协同频谱感知,但其能耗较大。这些额外的能量被用于感知频谱并向融合中心(FC)报告每个节点的本地决策。本文讨论了三种不同的总传感时间和能量减少或吞吐量提高方案。在这里,我们将讨论总体能量最小化和吞吐量最大化的遗传算法和整数线性规划方案。
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