Intra-Channel Nonlinearity Mitigation in Optical Fiber Transmission Systems Using Perturbation-Based Neural Network

Ding, Jiazheng; Liu, Tiegen; Xu, Tongyang; Hu, Wang; Popov, Sergei; Leeson, Mark S.; Zhao, Jian; Xu, Tianhua

doi:10.1109/jlt.2022.3200827

Cited by 13 publications

(5 citation statements)

References 66 publications

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“…In PDM optical fiber communication systems, the continuous signals can be denoted as T , where z and t represent the transmission distance and time, respectively. In optical fiber transmission link, the ⃗ Q(z, t) follows the Manakov equation as [17]:…”

Section: Feature Map Constructionmentioning

confidence: 99%

Global Receptive Field Designed Complex-Valued Convolutional Neural Network Equalizer for Optical Fiber Communications

Han,

Wang,

Yang

et al. 2024

Photonics

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In this paper, an improved complex-valued convolutional neural network (CvCNN) structure to be placed at the received side is proposed for nonlinearity compensation in a coherent optical system. This complex-valued global convolutional kernel-assisted convolutional neural network equalizer (CvGNN) has been verified in terms of Q-factor performance and complexity compared to seven other related nonlinear equalizers based on both the 64 QAM experimental platform and the QPSK numerical platform. The global convolution operation of the proposed CvGNN is more suitable for the calculation process of perturbation coefficients, and the global receptive field can also be more effective at extracting effective information from perturbation feature maps. The introduction of CvCNN can directly focus on the complex-valued perturbation feature maps themselves without separately processing the real and imaginary parts, which is more in line with the waveform-dependent physical characteristics of optical signals. Based on the experimental platform, compared with the real-valued neural network with small convolutional kernel (RvCNNC), the proposed CvGNNC improves the Q-factor by ∼2.95 dB at the optimal transmission power, while reducing the time complexity by ∼44.7%.

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Section: Feature Map Constructionmentioning

confidence: 99%

Global Receptive Field Designed Complex-Valued Convolutional Neural Network Equalizer for Optical Fiber Communications

Han,

Wang,

Yang

et al. 2024

Photonics

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“…After the transmission over the communication link dominated by ASE noise, the received symbol sequence 𝒀 = [𝑦 1 , … , 𝑦 𝑛 𝑠 ] is processed by a CM decoder, which provides the soft information of the transmitted information sequence. [13][14][15] The difference between the two symbols is defined as the vector 𝑑 𝑖,𝑗 = 𝑥 𝑖 − 𝑥 𝑗 . In this work, we consider the most common case in optical fiber communication systems, which is 𝑀 = 2, corresponding to coherent communication using two polarizations of the optical signal.…”

Section: System Model and Ase Noise 21 System Modelmentioning

confidence: 99%

Amplifier limited information rates in high-speed optical fiber communication systems

Liu

et al. 2022

Optoelectronic Devices and Integration XI

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Due to the high transmission capacity, optical fiber systems have been widely applied in the modern telecommunication infrastructure to meet the ever-increasing demand of data traffic. Optical amplifiers have been employed to amplify optical signals and to compensate for the transmission losses. They play a key role in relaying the signals in ultra-wideband optical fiber communication systems. However, the amplified spontaneous emission (ASE) noise will be introduced and will pose constraints on the transmission information rates. The mutual information (MI) and the generalized mutual information (GMI) have been applied to evaluate the information rates in communication systems. In this work, we have investigated the impact of ASE noise on the MI and the GMI, and developed corresponding analyses across different modulation formats. Our work aims to explore the limit and requirements on optical amplifiers in next-generation ultra-wideband optical fiber communication systems.

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“…Using neural networks to process the sequence of signal features is equivalent to gradually fusing and extracting the relationships between the features, in which the depth of the latter is well condensed. However, the breadth of the features (e.g., the power of the current signal) may be weakened during gradual feature fusion and extraction, which then impacts the nonlinear equalization results [18][19][20]. It has been shown that inter-channel crosstalk in wavelength division multiplexing WDM systems significantly affects transmission capacity and transmission distance.…”

Section: Introductionmentioning

confidence: 99%

Wide and Deep Learning-Aided Nonlinear Equalizer for Coherent Optical Communication Systems

Jiang,

Liu,

Zhang

2024

Photonics

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In this study, we developed a wide and deep network-based nonlinear equalizer to compensate for nonlinear impairment in coherent optical communication systems. In our proposed equalizer, the power feature factor and inter-symbol feature sequence in the received signal are analyzed by two combined networks, wide and deep, respectively, so that the information contained in the signal can be fully utilized. We designed an experiment using a 120 Gbit/s 64-quadrature amplitude modulation (64-QAM) coherent optical communication system over a 375 km standard single-mode fiber (SSMF) to verify the performance of the proposed wide and deep network-based nonlinear equalizer. The experimental results showed that the proposed wide and deep network-based nonlinear equalizer achieved better performance at lower complexity compared with the traditional neural network-based nonlinear equalizer. The proposed equalizer significantly improved the equalization effect at a cost of a 0.3% increase in parameters, which indicates the potential of the proposed method for application in coherent optical communication systems.

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Intra-Channel Nonlinearity Mitigation in Optical Fiber Transmission Systems Using Perturbation-Based Neural Network

Cited by 13 publications

References 66 publications

Global Receptive Field Designed Complex-Valued Convolutional Neural Network Equalizer for Optical Fiber Communications

Global Receptive Field Designed Complex-Valued Convolutional Neural Network Equalizer for Optical Fiber Communications

Amplifier limited information rates in high-speed optical fiber communication systems

Wide and Deep Learning-Aided Nonlinear Equalizer for Coherent Optical Communication Systems

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