Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems

Huang, Chaoran; Fujisawa, Shinsuke; Lima, Thomas Ferreira de; Tait, Alexander N.; Blow, Eric C.; Tian, Yue; Bilodeau, Simon; Jha, Aashu; Yaman, Fatih; Batshon, Hussam G.; Peng, Hsuan-Tung; Shastri, Bhavin J.; Inada, Yoshihisa; Wang, Ting; Prucnal, Paul R.

doi:10.1364/ofc.2020.th4c.6

Cited by 32 publications

(16 citation statements)

References 7 publications

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“…2 is represented by (3), and this layer is the core of the reservoir structure. Then it is followed by a static layer, represented by (4), which incorporates the leaky-ESN behavior through accumulating (integrating) its inputs, but it is also losing exponentially (leaking) accumulated excitation over time. Finally, the output layer defines which units are relevant to the description of the current task (for the equalization, in our case), and it is described by (5).…”

Section: Echo State Networkmentioning

confidence: 99%

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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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Section: Echo State Networkmentioning

confidence: 99%

“…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]. It is worth noticing that recent works evaluated NN-based equalizers in metro/longhaul trials [4], [5], [8]- [10].…”

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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“…where s is the neuron's state, y is output signal, τ is the time constant of the photonic circuit, W is the photonic weight, and σ(•) is the transfer function of the silicon photonic modulator neurons [10,12].…”

Section: Prnn Modelmentioning

confidence: 99%

A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge

Peng,

Lederman,

et al. 2021

Preprint

Self Cite

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Machine learning (ML) methods are ubiquitous in wireless communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and cognitive radio. However, the large size of ML models can make them difficult to implement on edge devices for latency-sensitive downstream tasks. In wireless communication systems, ML data processing at a sub-millisecond scale will enable real-time network monitoring to improve security and prevent infiltration. In addition, compact and integratable hardware platforms which can implement ML models at the chip scale will find much broader application to wireless communication networks. Toward real-time wireless signal classification at the edge, we propose a novel compact deep network that consists of a photonic-hardware-inspired recurrent neural network model in combination with a simplified convolutional classifier, and we demonstrate its application to the identification of RF emitters by their random transmissions. With the proposed model, we achieve 96.32% classification accuracy over a set of 30 identical ZigBee devices when using 50 times fewer training parameters than an existing state-of-the-art CNN classifier. Thanks to the large reduction in network size, we demonstrate real-time RF fingerprinting with 219 µs latency using a small-scale FPGA board, the PYNQ-Z1.

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“…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]. It is worth noticing that some very recent works evaluated the functioning of NN-based equalizers in metro/long-haul trials [4], [5], [8]- [10].…”

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

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.

show abstract

Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems

Abstract: We demonstrate the experimental implementation of photonic neural network for fiber nonlinearity compensation over a 10,080 km trans-pacific transmission link. Q-factor improvement of 0.51 dB is achieved with only 0.06 dB lower than numerical simulations.

Cited by 32 publications

References 7 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

A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge

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

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