Research on signal recovery method of IM/DD optical fiber transmission system based on multi-bit and multi-class classification convolutional neural network

Liang, Meng; Du, Junping

doi:10.1364/acpc.2020.m4a.231

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…It was demonstrated that the application of the NNs with different internal structures, such as multi-layer perceptron (MLP) [11], [12] (i.e. a simple densely connected feedforward NN architecture), convolutional NNs (CNN) [13], [14], echo state networks (ESN) [15], and long short-term memory (LSTM) NNs [16], is efficient in improving optical system-level performance. However, the test of similar NN architectures in coherent optical systems has been carried out, mainly, numerically [17]- [20], or in short-haul experiments [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Freire

Osadchuk

Spinnler

et al. 2021

J. Lightwave Technol.

125

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We present the results of the comparative performance-versus-complexity analysis for the several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison is carried out using an experimental set-up with the transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multilayer perceptron. Their performance is compared with the one delivered by the previously proposed NN-based equalizers: one biLSTM layer, three-dense-layer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. First, we present the general expressions for the computational complexity associated with each NN type; these are given in terms of real multiplications per symbol. We demonstrate that in the experimental system considered, the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the largest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). Then, we examine the trade-off between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to some lower levels, the three-layer perceptron provides the best performance.

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Section: Introductionmentioning

confidence: 99%

Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Freire

Osadchuk

Spinnler

et al. 2021

J. Lightwave Technol.

125

View full text Add to dashboard Cite

show abstract

“…In terms of the experimental verification of NN-based equalizers, several works dealt with the intensity-modulation with directdetection (IM/DD) links where the application of the NNs with different internal structure, such as multi-layer perceptron (MLP) [11], [12] (i.e. a simple densely connected feedforward NN architecture), convolutional NNs (CNN) [13], [14], echo state networks (ESN) [15] and long short-term memory (LSTM) NNs [16], have proven its effectiveness in improving optical system-level performance. However, the test of similar NN architectures in coherent optical systems has been carried out, mainly, numerically [17]- [20], or in short-haul experiments [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Freire,

Osadchuk,

Spinnler

et al. 2021

Preprint

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We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison has been carried out using an experimental set-up with transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multi-layer perceptron. Their performance is compared with the one delivered by the previously proposed NN equalizer models: one biLSTM layer, three-denselayer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. We present the derivation of the computational complexity associated with each NN type -in terms of real multiplications per symbol so that these results can be applied to a large number of communication systems. We demonstrated that in the specific considered experimental system the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the highest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). We examine the tradeoff between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to lower levels, the threelayer perceptron provides the best performance. Our complexity analysis for different NNs is generic and can be applied in a wide range of physical and engineering systems.

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Research on signal recovery method of IM/DD optical fiber transmission system based on multi-bit and multi-class classification convolutional neural network

Abstract: We have compared a new convolutional neural network based on multi-bit and multi-class classification of the time domain and wavelet domain feature extraction methods to recover the distorted signal in IM/DD optical fiber transmission system.

Cited by 2 publications

References 2 publications

Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

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