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Cooperative sensing based on permutation entropy with adaptive thresholding technique for cognitive radio networks

机译：基于置换熵和自适应阈值技术的认知无线电网络协同感知

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摘要

Spectrum sensing in the low signal-to-noise ratio (SNR) environment is vital task for the evolution of cognitive radio technology. The numerous signal processing algorithms have since been proposed to improve the spectrum sensing performance. In the recent past, entropy based sensing methods are shown to be robust in a low SNR environment with small data sets. However, these methods only focus on information content and ignore temporal order of the signal. Hence, selection of appropriate entropy technique that considers both information content and temporal order is important. In addition, many works consider that the distribution of noise follows Gaussian under assumption that the sample size is infinity. The detection threshold designed using this assumption yield unreliable decisions. On the contrary, the captured data is limited in real-time and it should be minimum to reduce the computational complexity. To address these two issues, empirical permutation entropy with adaptive thresholding detection technique is proposed. Then, the work is extended to weighted gain cooperative sensing that uses Higuchi fractal dimension method to generate weight for each node. Simulation results reveal that the proposed method is robust, less sensitive to sample size, and improves the single node as well as multinode sensing performance.
机译：在低信噪比（SNR）环境中的频谱感测对于认知无线电技术的发展至关重要。此后，提出了许多信号处理算法来改善频谱感测性能。在最近的过去中，基于熵的传感方法显示出在具有小数据集的低SNR环境中具有鲁棒性。但是，这些方法仅关注信息内容，而忽略了信号的时间顺序。因此，考虑信息内容和时间顺序的适当熵技术的选择很重要。另外，许多工作认为在样本大小为无穷大的假设下，噪声的分布遵循高斯分布。使用此假设设计的检测阈值会得出不可靠的决定。相反，捕获的数据是实时限制的，应该最小化以减少计算复杂性。针对这两个问题，提出了基于自适应阈值检测技术的经验置换熵。然后，将工作扩展到使用Higuchi分形维方法为每个节点生成权重的加权增益协作感知。仿真结果表明，该方法是鲁棒的，对样本量不那么敏感，并改善了单节点以及多节点的感知性能。

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