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首页> 外文期刊>Journal of optoelectronics and advanced materials >40 & 100 Gb/s optical communications systems based on blind support vector machine with electrical equalizer
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40 & 100 Gb/s optical communications systems based on blind support vector machine with electrical equalizer

机译：基于盲人支持向量机的电气均衡器40＆100 GB / S光通信系统

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摘要

This paper presents support vector machines (SVM) as a machine learning technique which is proposed as a method for performing nonlinear equalization in coherent optical CO-OFDM systems. The equalizer can offer a 7.75 dB improvement in optical signal-to-noise ratio (OSNR) compared to Volterra algorithm for 100 Gb/s CO-OFDM signals when considering only nonlinearities to achieve a Bit-Error-Ratio inferior to 10E-3. It is also revealed that SVM can double the transmission distance up to 1600 km over SMF compared to the case of Volterra and Hammerstein. The proposed algorithms are tested in simulation using a coherent 100 Gb/s 4-QAM optical communication system in a legacy optical network setting. We also present a blind-support vector machines (B-SVM) as a machine learning technique which is proposed as a method for performing nonlinear equalization in CO-OFDM systems. The B-SVM equalizer showed more efficiency in combating single mode fiber (SMF) induced nonlinearities compared to the Volterra. The B-SVM equalizer can reduce the fiber nonlinearity penalty by 1 dB at 2 dBm of launched power improvement compared to Volterra algorithm for 40 Gb/s CO-OFDM signals. It is also shown that SVM-NLE outperforms both V-NLE and full-field DBP-NLE in terms of Q-Factor by similar to 6 and 1.5 dB, respectively.
机译：本文将支持向量机（SVM）作为一种机器学习技术，提出作为在相干光学CO-OFDM系统中执行非线性均衡的方法。与100GB / S Co-OFDM信号的Volterra算法相比，均衡器可以提供7.75dB的改进，以便仅考虑非线性以实现差错率为10E-3的误码率。与Volterra和Hammerstein的情况相比，SVM也可以将传输距离与SMF相比翻转到1600公里。在传统光网络设置中使用相干的100 GB / S 4-QAM光通信系统在仿真中测试所提出的算法。我们还提出了一种作为机器学习技术的盲人支持向量机（B-SVM），该技术被提出为在CO-OFDM系统中执行非线性均衡的方法。与Volterra相比，B-SVM均衡器表明，对抗单模光纤（SMF）诱导的非线性的效率更高。与Volterra算法相比，B-SVM均衡器可以将光纤非线性损失1 dB降低1 dB，而40 Gb / s Co-OFDM信号。还示出了SVM-NLE分别通过类似于6和1.5dB的Q系数而优于V-NLE和全场DBP-NLE。

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