A cooperative blind spectrum detection with the Doolittle decomposition and support vector machine (SVM) classification is proposed to improve the performance and reduce the complexity and latency of spectrum detection in hybrid Geostationary Earth Orbit and Low Earth Orbit (GEO-LEO) satellite constellation networks. First, a principal component analysis (PCA) algorithm is adopted to process perceive signals. Second, the maximum-to-minimum eigenvalue ratio of a covariance matrix is utilized to construct statistics with self-learning ability by using the Doolittle decomposition. Third, primary user and noise signals are labeled with “+1” and “−1” as training labels, respectively. Finally, a high-performance PCA-SVM classification is generated by training the labels and the corresponding statistics. Innovations mainly include the Doolittle decomposition of the covariance matrix in the SVM and the PCA to preprocess the perceived signals. Simulation results show that the detection probability of the proposed scheme outperforms the energy detection and the SVM algorithm by 55 and 15% at a signal-to-noise ratio (SNR) of −15 dB, respectively. The average error probability of the proposed scheme is 20% lower than that of the SVM at an SNR of −20 dB. Therefore, in hybrid GEO-LEO satellite constellation networks, the cooperative blind spectrum detection with the Doolittle decomposition and SVM classification can detect the spectrum with low complexity and high performance effectively.

中文翻译：

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混合 GEO-LEO 卫星星座网络中采用 Doolittle 分解和 PCA-SVM 分类的协作盲谱检测


提出了一种采用杜立特分解和支持向量机（SVM）分类的协作盲频谱检测，以提高对地静止地球轨道和低地球轨道（GEO-LEO）混合卫星星座网络中频谱检测的性能并降低复杂性和延迟。首先，采用主成分分析（PCA）算法来处理感知信号。其次，利用杜立特分解，利用协方差矩阵的最大与最小特征值比来构造具有自学习能力的统计量。第三，主要用户和噪声信号分别标记为“+1”和“−1”作为训练标签。最后，通过训练标签和相应的统计数据生成高性能的 PCA-SVM 分类。创新主要包括SVM中协方差矩阵的Doolittle分解和PCA对感知信号的预处理。仿真结果表明，在信噪比（SNR）为-15 dB时，该方案的检测概率分别优于能量检测和SVM算法55%和15%。在信噪比为-20 dB 的情况下，所提出的方案的平均错误概率比 SVM 低 20%。因此，在混合GEO-LEO卫星星座网络中，结合杜立特分解和SVM分类的协同盲频谱检测可以有效地检测复杂度低、高性能的频谱。