Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine
Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine
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DOI：
10.1109/lpt.2018.2878530
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发表时间：
2018-12-15
影响因子：
2.6
通讯作者：
Li, Cheng
中科院分区：
文献类型：
--
作者：
Lin, Xiang;Dobre, Octavia A.;Li, Cheng
以接收信号幅度的累积分布函数为特征，提出了一种基于支持向量机的相干光通信系统调制格式联合分类和光信噪比估计算法。考虑了三种常用的正交幅度调制(QAM)格式。在光信噪比为5~30d B的范围内进行了数值仿真，结果表明，该算法获得了很好的调制分类性能和较高的光信噪比估计精度，最大估计误差为0.8 d B。还在感兴趣的光信噪比范围内进行了光学背靠背实验。对4-QAM、16-QAM和-QAM的平均信噪比估计误差分别为0.38、0.68和0.62dB。此外，与基于神经网络的联合估计算法相比，该算法在复杂度相当的情况下获得了更好的性能。
By adopting the cumulative distribution function of the received signal's amplitude as feature, a support vector machine-based algorithm is proposed to jointly classify the modulation format and estimate the optical signal-to-noise ratio (OSNR) in coherent optical communication systems. Three commonly-used quadrature-amplitude modulation (QAM) formats are considered. Numerical simulations have been carried out in the OSNR ranges from 5 to 30 dB, and results show that the proposed algorithm achieves a very good modulation classification (MC) performance, as well as high OSNR estimation accuracy with a maximum estimation error of 0.8 dB. Optical back-to-back experiments are also conducted in OSNR ranges of interest. A 99% average correct MC rate is observed, and mean OSNR estimation errors of 0.38, 0.68, and 0.62 dB are noticed for 4-QAM, 16-QAM, and 64-QAM, respectively. Furthermore, compared with the neural networks-based joint estimation algorithm, the proposed algorithm attains better performance with comparable complexity.