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Secure radio resource management in cloud computing based cognitive radio networks.

机译:基于云计算的认知无线电网络中的安全无线电资源管理。

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摘要

With the rapid development of cognitive radios, spectrum efficiency in cognitive radio networks (CRN) has increased by secondary users (SU) accessing the licensed spectrum dynamically and opportunistically without creating harmful interference to primary users. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity. In this paper, we propose secure radio resource management algorithm for CRN where cloud computing unit stores the spectrum occupancy information of heterogeneous wireless networks in CRN and facilitates the access of spectrum opportunities for secondary users. The proposed algorithm leverages the geolocation of secondary user and idle licensed bands to facilitate the secure allocation of radio resources to SU. Furthermore, the secondary users who provide high benefit are admitted while satisfying the quality of service (QoS) requirement of secondary users in terms of data rate and service time. We also present the design to implement the proposed algorithm on Cloud computing platform, and propose a scalable mapping method under the Storm, realtime processing model to dynamically partition the geographical area according to the SU density. Simulation results are presented to demonstrate the performance of the proposed secure radio resource management algorithm.
机译:随着认知无线电的快速发展,辅助用户(SU)动态地和机会性地访问许可频谱而不增加对主要用户的有害干扰,从而提高了认知无线电网络(CRN)的频谱效率。但是,CRN的性能和安全性受到其有限的功率,内存和计算能力的很大限制。幸运的是,由于云计算的巨大存储和计算能力,它的出现可以缓解这些限制。在本文中,我们提出了一种用于CRN的安全无线资源管理算法,其中,云计算单元将异构无线网络的频谱占用信息存储在CRN中,并方便了次要用户的频谱机会。所提出的算法利用辅助用户和空闲许可频段的地理位置来促进向SU安全分配无线资源。此外,在数据速率和服务时间方面满足二级用户的服务质量(QoS)要求的同时,接纳提供高利益的二级用户。我们还提出了在云计算平台上实现该算法的设计,并提出了一种在Storm实时处理模型下的可扩展映射方法,以根据SU密度动态划分地理区域。仿真结果表明了所提出的安全无线资源管理算法的性能。

著录项

  • 作者

    Raza, Khurram.;

  • 作者单位

    Tennessee State University.;

  • 授予单位 Tennessee State University.;
  • 学科 Engineering Computer.;Computer Science.
  • 学位 M.S.
  • 年度 2013
  • 页码 113 p.
  • 总页数 113
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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