Differential Entropy-Driven Spectrum Sensing Under Generalized Gaussian Noise
Differential Entropy-Driven Spectrum Sensing Under Generalized Gaussian Noise
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DOI：
10.1109/lcomm.2016.2564968
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Sanjeev Gurugopinath;R. Muralishankar;H. N. Shankar
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作者：
Sanjeev Gurugopinath;R. Muralishankar;H. N. Shankar
我们提出了一种新的拟合优度检测方案的频谱感知，基于差分熵在接收到的意见。已知在许多实际通信设置中噪声分布偏离高斯分布。因此，我们允许噪声过程遵循广义高斯分布，其中包括高斯和拉普拉斯作为特殊情况。我们得到，在封闭的形式下，分布的检验统计量的零假设和计算的检测阈值，满足约束的虚警概率。此外，我们推导出在一般情况下的检测概率的下限，使用熵功率不等式。通过Monte Carlo仿真，我们表明，对于一类实际相关的衰落信道和主信号模型，特别是在低信噪比的制度，我们的检测器实现了更高的检测概率比能量检测器和基于阶跃的检测器。我们还证明，与能量检测器相比，提出的检测器的噪声方差不确定性的不利影响要小得多。
We propose a novel goodness-of-fit detection scheme for spectrum sensing, based on differential entropy in the received observations. The noise distribution is known to deviate from the Gaussian in many practical communication settings. We, therefore, permit that the noise process follows the generalized Gaussian distribution, which subsumes Gaussian and Laplacian as special cases. We obtain, in closed form, the distribution of the test statistic under the null hypothesis and compute the detection threshold that satisfies a constraint on the probability of false alarm. Furthermore, we derive a lower bound on the probability of detection in a general scenario, using the entropy power inequality. Through Monte Carlo simulations, we show that for a class of practically relevant fading channel and primary signal models, especially in low SNR regime, our detector achieves a higher probability of detection than the energy detector and the order statistics-based detector. We also demonstrate that the adverse effect of noise variance uncertainty is much less with the proposed detector compared with that of the energy detector.