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摘要翻译：
频谱感知是认知无线电中的一个基本问题。提出了一种基于协方差矩阵函数的认知无线电网络频谱感知检测算法。该算法以包含迹运算的矩阵函数的单调递增性质为基础。该算法的优点是可以在极低的信噪比下工作，如在样本数据有限的情况下，信噪比低于-30 dB。对该算法的阈值设置进行了理论分析。通过对捕获的数字电视(DTV)信号进行仿真，比较了该算法与其它现有算法的性能。
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英文标题：
《Generalized FMD Detection for Spectrum Sensing Under Low Signal-to-Noise
Ratio》
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作者：
Feng Lin, Robert C. Qiu, Zhen Hu, Shujie Hou, James P. Browning,
Michael C. Wicks
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. The advantage of proposed algorithm is it works under extremely low signal-to-noise ratio, like lower than -30 dB with limited sample data. Theoretical analysis of threshold setting for the algorithm is discussed. A performance comparison between the proposed algorithm and other state-of-the-art methods is provided, by the simulation on captured digital television (DTV) signal.
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PDF链接：
https://arxiv.org/pdf/1202.4190
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