Kernelized Generalized Likelihood Ratio Test for Spectrum Sensing in Cognitive Radio
Kernelized Generalized Likelihood Ratio Test for Spectrum Sensing in Cognitive Radio
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DOI：
10.1109/tvt.2018.2824023
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发表时间：
2018-04
影响因子：
6.8
通讯作者：
Lily Li;S. Hou;A. Anderson
中科院分区：
文献类型：
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作者：
Lily Li;S. Hou;A. Anderson
在下一代无线电网络中，频谱感知被认为是解决频谱稀缺问题的关键技术。不幸的是，许多频谱感测方法在低信噪比（SNR）环境中不能很好地工作。针对这一问题，提出并分析了一种新的认知无线电频谱感知算法-核化广义似然比检验（KGLRT）。有效地，KGLRT使用一个非线性内核输入数据映射到一个高维特征空间，然后，广泛接受的（线性）广义似然比检验用于假设检验。这种新的算法给出了一个增益为4 dB的信噪比比其线性对应。本文首次对该算法进行了理论分析，并与图像信号处理中的算法进行了比较。发现检测度量是集中随机变量，并证明了检测度量的概率分布服从F分布，这与利用集中不等式得到的结果一致.针对目标虚警概率推导出分析阈值。阈值与噪声功率无关;因此，所提出的算法可以克服极低SNR水平下的噪声不确定性问题。仿真验证了理论结果。
Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. The thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results.