Fibre impairment compensation using artificial neural network equalizer for high-capacity coherent optical OFDM signals

M. Jarajreh, S. Rajbhandari, E. Giacoumidis, N. Doran, Zabih Ghassemlooy
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引用次数: 5

Abstract

We propose an artificial neural network (ANN) equalizer for transmission performance enhancement of coherent optical OFDM (C-OOFDM) signals. The ANN equalizer showed more efficiency in combating both chromatic dispersion (CD) and single-mode fibre (SMF)-induced non-linearities compared to the least mean square (LMS). The equalizer can offer a 1.5 dB improvement in optical signal-to-noise ratio (OSNR) compared to LMS algorithm for 40 Gbit/s C-OOFDM signals when considering only CD. It is also revealed that ANN can double the transmission distance up to 320 km of SMF compared to the case of LMS, providing a nonlinearity tolerance improvement of ~0.7 dB OSNR.
基于人工神经网络均衡器的高容量相干光OFDM信号光纤损伤补偿
针对相干光OFDM (C-OOFDM)信号的传输性能增强,提出了一种人工神经网络均衡器。与最小均方(LMS)相比,ANN均衡器在对抗色散(CD)和单模光纤(SMF)引起的非线性方面表现出更高的效率。在仅考虑CD的情况下,该均衡器在40 Gbit/s C-OOFDM信号的光信噪比(OSNR)比LMS算法提高1.5 dB。研究还表明,与LMS相比,ANN算法可以将SMF的传输距离提高一倍,最高可达320 km,非线性容差提高约0.7 dB OSNR。
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