Intelligent constellation diagram analyzer using convolutional neural network-based deep learning

Wang, Danshi; Zhang, Min; Li, Jin; Li, Ze; Song, Chaosheng; Xue, Chengqi

doi:10.1364/oe.25.017150

Cited by 215 publications

(86 citation statements)

References 22 publications

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“…Moreover, the results show that a larger training dataset and a deeper neural network can help to increase the estimation performance. As more advanced neural network structures emerge, CNN is also introduced to monitor the OSNR and modulation format simultaneously [13,58,59]. In [37], ANN is adopted to monitor the OSNR based on the historical data collected from real systems.…”

Section: Ai-based Qot and Impairment Monitoringmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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Section: Ai-based Qot and Impairment Monitoringmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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“…The neural network (NN) is computational model loosely inspired by its biological counterparts [88]. In recent years, it has been proposed to mitigate the nonlinear impairments in optical communication system [89][90][91]. For short-reach PAM4 optical links, various research concerning the NN method has been performed to improve transmission performance [43][44][45][46][47][48][49][50][51][52][53].…”

Section: Neural Networkmentioning

confidence: 99%

Recent Advances in Equalization Technologies for Short-Reach Optical Links Based on PAM4 Modulation: A Review

Zhou

Liu

et al. 2019

Applied Sciences

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In recent years, short-reach optical links have attracted much more attention and have come to constitute a key market segment due to the rapid development of data-center applications and cloud services. Four-level pulse amplitude modulation (PAM4) is a promising modulation format to provide both a high data rate and relatively low cost for short-reach optical links. However, the direct detector and low-cost components also pose immense challenges, which are unforeseen in coherent transmission. To compensate for the impairments and to truly meet data rate requirements in a cost-effective manner, various digital signal processing (DSP) technologies have been proposed and investigated for short-reach PAM4 optical links. In this paper, an overview of the latest progress on DSP equalization technologies is provided for short-reach optical links based on PAM4 modulation. We not only introduce the configuration and challenges of the transmission system, but also cover the principles and performance of different equalizers and some improved methods. Moreover, machine learning algorithms are discussed as well to mitigate the nonlinear distortion for next-generation short-reach PAM4 links. Finally, a summary of various equalization technologies is illustrated and our perspective for the future trend is given. Appl. Sci. 2019, 9, 2342 2 of 22 multi-tone (DMT), and quadrature amplitude modulation (QAM) based on Kramers-Kronig (KK) receiver [18]. Considering the power consumption and implementation complexity, the most attractive format in short-reach optical links is PAM4, which is believed as a highly promising candidate for the next-generation passive optical network (NG-PON) and has been ratified by IEEE 802.3 bs for 400 Gbps Ethernet transmission [19][20][21][22][23][24][25].However, the low-pass filtering effects induced by the limited bandwidth of transmitter and receiver can cause severely inter-symbol interference (ISI). Low-cost devices such as lasers, modulators, photodiodes (PD) and trans-impedance amplifiers (TIA) also produce nonlinear distortions like level-dependent skew and level-dependent noise. Furthermore, the interaction between chromatic dispersion (CD) and direct detection will lead to a power-fading effect, where the detected signal may contain frequency notches after several kilometers transmission at a high symbol rate. Therefore, various equalization technologies based on digital signal processing (DSP) have been investigated for short-reach links over the years . Conventional equalizers, such as the feed-forward equalizer (FFE) or decision feedback equalizer (DFE), are employed to compensate for the linear impairments induced by limited bandwidth and CD [26][27][28], while the equalizer based on the Volterra series is utilized to mitigate nonlinear distortion [29][30][31][32]. Some new equalization techniques, such as direct detection-faster than Nyquist (DD-FTN) algorithm [33][34][35], intensity directed FFE/DFE (ID-FFE/ID-DFE) algorithm [36,37] and joint clock recovery and FFE (CR-FFE) algorithm...

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“…We also demonstrate that optimizing irregular low-density parity-check (LDPC) codes to match EXIT chart of the DNN-TEQ can improve achievable rates by up to 0.12 b/s/Hz. M ACHINE learning techniques [1]- [3] have been recently applied to optical communications systems to deal with various issues such as network monitoring [4]- [6], traffic control [7]- [10], signal design [11]- [15], and nonlinearity compensation [16]- [21]. Since the fiber nonlinearity is a major limiting factor to the achievable information rates [22]- [24], mitigating nonlinearity has been of great importance to realize high-speed, reliable, and long-reach optical communications.…”

mentioning

confidence: 99%

Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

Koike‐Akino

Wang

Millar

et al. 2020

J. Lightwave Technol.

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Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts. The application of deep neural networks (DNN) allows flexible statistical analysis of complicated fiber-optic systems without relying on any specific physical models. Due to the inherent nonlinearity in DNN, various equalizers based on DNN have shown significant potentials to mitigate fiber nonlinearity. In this paper, we propose a turbo equalization (TEQ) based on DNN as a new alternative framework to deal with nonlinear fiber impairments for future coherent optical communications. The proposed DNN-TEQ is constructed with nested deep residual networks (ResNet) to train extrinsic likelihood given soft-information feedback from channel decoding. Through extrinsic information transfer (EXIT) analysis, we verify that our DNN-TEQ can accelerate decoding convergence to achieve a significant gain in achievable throughput by 0.61 b/s/Hz. We also demonstrate that optimizing irregular low-density parity-check (LDPC) codes to match EXIT chart of the DNN-TEQ can improve achievable rates by up to 0.12 b/s/Hz.

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Intelligent constellation diagram analyzer using convolutional neural network-based deep learning

Cited by 215 publications

References 22 publications

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Recent Advances in Equalization Technologies for Short-Reach Optical Links Based on PAM4 Modulation: A Review

Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

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