Energy-Decisive and Upgrade Cooperative Spectrum Sensing in Cognitive Radio Networks

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• 作者：Alpa Chaudhary ; ^{alpachaudhary87@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Manoj Dongre ; Hemlata Patil
• 关键词：Cooperative spectrum sensing ; Energy-Efficiency ; Sensing time ; Cognitive radio network
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：79
• 期：Complete
• 页码：683-691
• 全文大小：464 K

文摘

In recent year energy efficiency and sensing time are become disparaging corner for cognitive radio (CR) networks, as network become deliberately energy-onerous. Cognitive radio intensify the pliability of personal services through language that illustrate knowledge of radio protocol, software module, proliferations, networks, user needs, and application scenario in a way that supports automated reasoning about the requirements of users [1]. As fast growing wireless applications are consuming more and more energy, and impersonate big challenges to operators in terms of energy footmark. Energy efficiency is not only includes the greenhouse problem and operational outlay, but is an obligatory to limit the power consumption demand in spectrum sensing and signal overhead, so it is of preeminent priority for a CR scenario compared to non-CR ones. Short while ago cooperative spectrum sensing is presented as a productive way to achieve superior sensing precision and bring down energy depletion by harnessing spatial diversity. So here we discussed the implications of facilitating higher energy efficiency in cognitive radio network from the perspective of fundamental trade-offs (i.e. what need to sacrifices to be energy efficient). In this review paper we have modelled given optimization problem with two different strategies. In first strategy only one phase of coarse spectrum sensing is activated in situation of absence of primary user or Signal-to-Noise Ratio (SNR) quantity is quite large. The second strategy accomplished for quality spectrum sensing. Here only single bit result send to the fusion center (FC), which overcome the consumption. And next algorithm finely exploit the local outcomes of coarse detection. It conserves the energy and improves a detection performance in noticeable amount. Simulation results shows that proposed strategies can achieve goal of minimum energy, less sensing time and better performance.