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基于机器学习的认知无线电网络协作频谱检测

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摘要 为提高认知无线电频谱感知能力,解决频谱感知节点对频谱占用的错误分类,提出了一种基于机器学习的认知无线电网络协作频谱感知方法。首先,构建了一个认知无线电网络模型,并运用Kullback-Leibler散度,推导出了单个感知节点频谱感知覆盖的解析式。其次,分析了无线传感器网络的频谱感知范围和性能,提出了一种能够覆盖整个主要用户区域的多感知节点布置策略。最后,通过对比实验验证了所提方法的有效性。实验结果表明,所提方案通过多部署一个感知节点SN3能够有效缓解频谱错误分类问题,对于50mW和100mW的主要用户功率电平,其真阳率分别可达99.80%与97.5%,可有效提升频谱感知的精确度。
出处 《长江信息通信》 2023年第1期133-136,共4页 Changjiang Information & Communications
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