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Efficient cooperative spectrum sensing in Cognitive Radio using coalitional game model

机译:基于联盟博弈模型的认知无线电中有效的协作频谱感知

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The performance of spectrum sensing is limited by noise uncertainty, multipath fading, and shadowing, which are the fundamental characteristics of wireless channels. If the Primary User (PU) signal experiences deep fading or blocked by obstacles, the power of the received PU signal at the Secondary User (SU) may be too weak to be detected. If the SU transmitter cannot detect the presence of the PU transmitter while the PU receiver is within the transmission range of the SU, the transmission of the PU will be interfered. To address this problem, CSS has been shown to be an effective method to improve the detection performance by exploiting spatial diversity. In this paper, we investigate the optimality of cooperation scheme using coalitional game theory. The cognitive behavior i.e. observe, learn and act can be modelled effectively in game theory to study, model and analyze the strategic interactions among Cognitive Radio (CR) users. This paper describes the analytical insights of coalitional game theory for CSS in cognitive radio, highlighting techniques for identifying steady states, convergence and steady state optimality. We critically assess the techniques available in the literature, and then describe our ongoing work applying our insights to algorithm design for cognitive radios using Game theory.
机译:频谱感测的性能受到噪声不确定性,多径衰落和阴影的限制,这是无线信道的基本特征。如果主要用户(PU)信号经历深度衰减或被障碍物阻塞,则次要用户(SU)上接收到的PU信号的功率可能太弱而无法检测到。如果在PU接收机处于SU的传输范围内时SU发射机无法检测到PU发射机的存在,则PU的传输将受到干扰。为了解决这个问题,CSS已被证明是通过利用空间分集来提高检测性能的有效方法。在本文中,我们使用联盟博弈理论研究合作方案的最优性。可以在博弈论中有效地对认知行为(即观察,学习和行动)进行建模,以研究,建模和分析认知无线电(CR)用户之间的战略互动。本文介绍了认知无线电中CSS的联盟博弈论的分析见解,重点介绍了识别稳态,收敛性和稳态最优性的技术。我们批判性地评估了文献中可用的技术,然后使用博弈论将我们的见解应用于认知无线电的算法设计来描述我们正在进行的工作。

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