An Optical Communication's Perspective on Machine Learning and Its Applications

Khan, Faisal Nadeem; Fan, Qirui; Lu, Chao; Lau, Alan Pak Tao

doi:10.1109/jlt.2019.2897313

Cited by 260 publications

(149 citation statements)

References 63 publications

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“…In this case, data-driven ML methods are essential tools for network planning and management, but these methods should be improved to be cost-effective and reliable for deployment. Several previous review works have provided comprehensive summaries of the applications of ML techniques in optical networks [2,[16][17][18][19]. They discuss the ML-based techniques adopted in various domains and point out many possible directions for the future deployment strategies.…”

Section: 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: 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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“…10% of data samples were randomly selected from the training dataset and allocated as the validation dataset to provide unbiased evaluation while tuning the ANN model parameters (weights and biases). Rectified linear unit (ReLU) [29] activation function and Adam [30] optimizer were used for approximating the non-linear function and to optimize the weights during the training process, respectively. ANN model predicts some outputs after each iteration/epoch.…”

Section: Pcf Modeling With Annmentioning

confidence: 99%

Machine learning approach for computing optical properties of a photonic crystal fiber

et al. 2019

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Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 μm, pitch from 0.8-2.0 μm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical Mode Solutions are also compared.

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“…where |T e | is the number of fully estimated messages in the test sequence and I t¨u denotes the indicator function, equal to 1 when the argument is satisfied and 0 otherwise. Figure 4 (top) shows the improvement in BLER at different distances for W " 10 after optimization of the coefficients a pqq in (2). As a reference we used the BLER performances when equal weights a pqq " 1 W , q " 0, .…”

Section: B Sliding Window Sequence Estimation Algorithmmentioning

confidence: 99%

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

Karanov

Liga

Aref

et al. 2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/DD) links. We consider an autoencoder based on the recently proposed sliding window bidirectional recurrent neural network (SBRNN) design to realize the transceiver for optical IM/DD communication. We show that its performance can be improved by introducing a weighted sequence estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping optimization to reduce the bit-error rate (BER) of the system. Furthermore, we carry out a detailed comparison with classical schemes based on pulse-amplitude modulation and maximum likelihood sequence detection (MLSD). Our investigation shows that for a reference 42 Gb/s transmission, the SBRNN autoencoder achieves a BER performance comparable to MLSD, when both systems account for the same amount of memory. In contrast to MLSD, the SBRNN performance is achieved without incurring a computational complexity exponentially growing with the processed memory.

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An Optical Communication's Perspective on Machine Learning and Its Applications

Cited by 260 publications

References 63 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

Machine learning approach for computing optical properties of a photonic crystal fiber

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

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