...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Distributed Sensor Networks >A Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Network Using Eigenvalue Detection Technique with Superposition Approach
【24h】

A Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Network Using Eigenvalue Detection Technique with Superposition Approach

机译：具有叠加方法特征值检测技术的认知无线网络中基于群集的协作频谱感测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cognitive radio (CR) networks have been active area of research because of its ability to opportunistically share the spectrum. A cluster-based cooperative spectrum sensing (CCSS) has a tremendous impact on sensing reliability compared with cooperative spectrum sensing. The energy detection (ED) technique requires perfect knowledge of noise power. An eigenvalue-based spectrum sensing has mitigated the noise uncertainty problem. Sensing and reporting time slots are rigidly separated in the conventional ED and eigenvalue-based detection (EVD) schemes. In CCSS, more reporting time slots are required as the number of CR users (CRUs) increases. If the reporting time slots of other CRUs as sensing time slots with a superposition allocation, the more reliable channel sensing can be achieved. In this paper, we propose CCSS using EVD technique with a superposition approach scheme where the reporting time slot is properly utilized to sense the primary user’s (PU’s) signal more accurately by rescheduling the reporting time slot for CRUs and cluster heads (CHs). Simulation result shows that the proposed EVD scheme has better detection probability than the conventional CCSS using both ED and EVD techniques.
机译：认知无线电（CR）网络一直是研究领域，因为它能够机会分享光谱。与协作频谱感测相比，​​基于群集的协作频谱感测（CCSS）对传感可靠性产生巨大影响。能量检测（ED）技术需要完美的噪声功率知识。基于特征值的频谱传感已经缓解了噪声不确定性问题。在传统的ED和基于特征值的检测（EVD）方案中，感测和报告时隙刚性分开。在CCSS中，随着CR用户（CRU）的数量增加，需要更多报告时隙。如果其他CRU的报告时隙作为具有叠加分配的感测时隙，则可以实现更可靠的频道感测。在本文中，我们使用具有EVD技术的CCSS使用叠加方法方案，其中通过重新安排Crus和群集头（CHS）来正确地利用报告时隙来更准确地感测主用户的（PU）信号。仿真结果表明，所提出的EVD方案具有比使用ED和EVD技术的传统CCS更好的检测概率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号