A modulation classification method in combination with partition-fractal and support-vector machine(SVM)learning methods is proposed to realize no prior recognition of the modulation mode in satellite laser communicat...A modulation classification method in combination with partition-fractal and support-vector machine(SVM)learning methods is proposed to realize no prior recognition of the modulation mode in satellite laser communication systems. The effectiveness and accuracy of this method are verified under nine modulation modes and compared with other learning algorithms. The simulation results show when the signal-to-noise ratio(SNR) of the modulated signal is more than 8 dB, the classifier accuracy based on the proposed method can achieve more than 98%, especially when in binary phase shift keying and quadrature amplitude shift keying modes, and the classifier achieves 100% identification whatever the SNR changes to. In addition, the proposed method has strong scalability to achieve more modulation mode identification in the future.展开更多
基金supported in part by the National Natural Science Foundation of China (NSFC)(Nos. 61675033,61835002,61727817,and 61675232)。
文摘A modulation classification method in combination with partition-fractal and support-vector machine(SVM)learning methods is proposed to realize no prior recognition of the modulation mode in satellite laser communication systems. The effectiveness and accuracy of this method are verified under nine modulation modes and compared with other learning algorithms. The simulation results show when the signal-to-noise ratio(SNR) of the modulated signal is more than 8 dB, the classifier accuracy based on the proposed method can achieve more than 98%, especially when in binary phase shift keying and quadrature amplitude shift keying modes, and the classifier achieves 100% identification whatever the SNR changes to. In addition, the proposed method has strong scalability to achieve more modulation mode identification in the future.
