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采用无线电环境地图和神经网络的合作多带频谱传感.

Yanqueleth Molina-Tenorio1, Alfonso Prieto-Guerrero2, Rafael Aguilar-Gonzalez3,4

  • 1Information Science and Technology Ph.D., Metropolitan Autonomous University, Mexico City 09360, Mexico.

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概括

认知无线电网络 (CRN) 通过样本和神经网络准确地检测主要用户 (PU) 和频谱漏洞. 神经网络在识别PU载波频率和带宽方面提供了卓越的准确性,以实现高效的二级用户 (SU) 频谱访问.

关键词:
认知无线电可以使用.合作的传感器网络.多频谱频谱传感器神经网络的神经网络的神经网络无线电环境地图 无线电环境地图实时实现实时实施.

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科学领域:

  • 无线通信工程 无线通信工程
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 认知无线电网络 (CRN) 对于高效的频谱利用至关重要.
  • 准确检测主要用户 (PU) 和频谱漏洞对于次要用户 (SU) 来说至关重要.
  • 现有的方法需要强大的实时频谱监控能力.

研究的目的:

  • 为实时多频段频谱监控提出和实施一个集中式CRN.
  • 开发射电环境地图 (REM) 用于频谱差距的识别.
  • 为了比较PU检测的数字信号处理 (DSP) 和神经网络 (NN) 方法.

主要方法:

  • 在真正的无线环境中使用软件定义无线电 (SDR).
  • 采用样本来监测局部频谱占用率的USS.
  • 使用DSP和NN技术进行中央集中的数据处理,用于PU特征提取 (功率,带宽,中心频率).

主要成果:

  • DSP和基于NN的CRN都成功地定位了PU并确定了光谱差距.
  • 基于NN的CRN在检测PU载波频率和带宽方面表现出卓越的准确性.
  • 该系统通过提供准确的频谱可用性信息,有效避免了隐藏的终端问题.

结论:

  • 神经网络为CRN中PU检测提供了一个高度准确的方法.
  • 使用NN的集中频谱监控可以提高CRN的性能和频谱效率.
  • 拟议的系统有效地绘制了用于动态频谱访问的无线电环境.