The principle of orthogonal frequency-division multiplexing (OFDM) is to transmit the data through a large number of multiple orthogonal subcarriers. The coherent optical OFDM (CO-OFDM) is OFDM data that are being modulated to light frequency and being detected in coherent manner. CO-OFDM brings to optical communications the combination of two powerful techniques, coherent optical detection and OFDM. One of the primary challenges in the CO-OFDM system is to remove optical fiber nonlinear effects. This makes nonlinearity compensation a critical task of the CO-OFDM system. So a nonlinear equalizer (NLE) based on adaptive neuro-fuzzy inference system (ANFIS) is presented for CO-OFDM systems to mitigate nonlinearities on long-haul optical communications with high bit rate and bit error rate (BER)of the system. Various performance metrics were analyzed for the proposed ANFIS–NLE, and it is compared with existing techniques such as support vector machine and artificial neural network. From the experimental results, our proposed approach gives better performance in terms of BER and Q-factor on comparing with existing methods.

中文翻译：

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基于自适应神经模糊推理系统的CO-OFDM系统非线性均衡器

正交频分复用（OFDM）的原理是通过大量的多个正交子载波传输数据。相干光学OFDM（CO-OFDM）是被调制为光频率并以相干方式检测的OFDM数据。CO-OFDM将相干光检测和OFDM这两种强大技术的结合带到了光通信中。CO-OFDM系统的主要挑战之一是消除光纤非线性效应。这使得非线性补偿成为CO-OFDM系统的关键任务。因此，针对CO-OFDM系统，提出了一种基于自适应神经模糊推理系统（ANFIS）的非线性均衡器（NLE），以减轻系统的高比特率和误码率（BER）对长距离光通信的非线性。针对拟议的ANFIS-NLE分析了各种性能指标，并将其与支持向量机和人工神经网络等现有技术进行了比较。从实验结果来看，与现有方法相比，我们提出的方法在BER和Q因子方面具有更好的性能。