Poor accuracy at low SNR makes fast autonomous wideband sensing in real-time difficult for cognitive radios. Support Vector Machines (SVM) can be an alternative in learning this sensing environment. But online training with conventional Eigen features has high complexity. This letter develops and validates on real-world data an efficient quick learning SVM-based blind sensing model using two new simple statistical features that can accurately detect signals at low SNR. Named as Smoothed Correlation of Reversed Spectrum Segments (SCRSS) and Variance of Multi-Scale Moving Averages (VMMA), they can speed up sensing to almost five times that of Eigen learning. With reduced computational cost, they appear to be promising in next-generation intelligent cognitive radio networks.

中文翻译：

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基于统计特征的 SVM 宽带传感

低 SNR 下的准确性差使得认知无线电难以实时进行快速自主宽带感知。支持向量机 (SVM) 可以作为学习这种传感环境的替代方法。但是具有传统特征特征的在线训练具有很高的复杂性。这封信使用两个新的简单统计特征开发并验证了基于真实世界数据的高效快速学习基于 SVM 的盲传感模型，该模型可以在低 SNR 下准确检测信号。它们被称为反向频谱段的平滑相关性 (SCRSS) 和多尺度移动平均方差 (VMMA)，它们可以将感知速度提高到特征学习的近五倍。随着计算成本的降低，它们在下一代智能认知无线电网络中似乎很有前途。