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Adaptive Neuro-Fuzzy Inference System-based Nonlinear Equalizer for CO-OFDM Systems

机译:基于自适应神经模糊推理系统的CO-OFDM系统的非线性均衡器

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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.
机译:正交频分复用(OFDM)的原理是通过大量多个正交的子载波发送数据。相干光学OFDM(CO-OFDM)是OFDM数据,其被调制为光频率并以相干方式检测。 Co-OFDM带来光学通信两种强大的技术,相干光学检测和OFDM的组合。 Co-OFDM系统中的主要挑战之一是去除光纤非线性效应。这使得非线性补偿成为Co-OFDM系统的关键任务。因此,提供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)用于CO-OFDM系统,以减轻与系统的高比特率和误码率(BER)的长途光通信上的非线性。针对所提出的ANFIS-NLE分析了各种性能指标,并将其与现有技术(如支持向量机和人工神经网络)进行比较。从实验结果来看,我们所提出的方法在比较现有方法方面提供了更好的性能和Q系数。

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