Machine learning for future fiber-optic communication systems /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Lau, Alan Pak Tao, Kham, Faisal Nadeem
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, United Kingdom ; San Diego, CA : Elsevier Academic Press, [2022]
Temas:
Optical fiber communication.
Machine learning.
T�el�ecommunications par fibres optiques.
Apprentissage automatique.
Machine learning
Optical fiber communication
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Machine Learning for Future Fiber-Optic Communication Systems
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Acknowledgments
  • 1 Introduction to machine learning techniques: An optical communication's perspective
  • 1.1 Introduction
  • 1.2 Supervised learning
  • 1.2.1 Artificial neural networks (ANNs)
  • 1.2.2 Choice of activation functions
  • 1.2.3 Choice of loss functions
  • 1.2.4 Support vector machines (SVMs)
  • 1.2.5 K-nearest neighbors (KNN)
  • 1.3 Unsupervised learning
  • 1.3.1 K-means clustering
  • 1.3.2 Expectation-maximization (EM) algorithm
  • 1.3.3 Principal component analysis (PCA)
  • 1.3.4 Independent component analysis (ICA)
  • 1.4 Reinforcement learning (RL)
  • 1.5 Deep learning techniques
  • 1.5.1 Deep learning vs. conventional machine learning
  • 1.5.2 Deep neural networks (DNNs)
  • 1.5.3 Convolutional neural networks (CNNs)
  • 1.5.4 Recurrent neural networks (RNNs)
  • 1.5.5 Generative adversarial networks (GANs)
  • 1.6 Future role of ML in optical communications
  • 1.7 Online resources for ML algorithms
  • 1.8 Conclusions
  • 1.A
  • References
  • 2 Machine learning for long-haul optical systems
  • 2.1 Introduction
  • 2.2 Application of machine learning in perturbation-based nonlinearity compensation
  • 2.2.1 Wide &amp
  • deep neural network
  • 2.2.2 Data collection and pre-processing
  • 2.2.3 Training results
  • 2.2.4 Results and discussion
  • 2.3 Application of machine learning in digital backpropagation
  • 2.3.1 Physics-based machine-learning models
  • 2.3.2 Single-polarization systems
  • 2.3.3 Dual-polarization systems
  • 2.3.4 Subband processing via filter banks
  • 2.3.5 Training and application examples
  • 2.4 Outlook of machine learning in long-haul systems
  • References
  • 3 Machine learning for short reach optical fiber systems
  • 3.1 Introduction to optical systems for short reach.
  • 3.2 Deep learning approaches for digital signal processing
  • 3.3 Optical IM/DD systems based on deep learning
  • 3.3.1 ANN receiver
  • 3.3.1.1 PAM transmission
  • 3.3.1.2 Sliding window FFNN processing
  • 3.3.2 Auto-encoders
  • 3.3.2.1 Auto-encoder design based on a feed-forward neural network
  • 3.3.2.2 Auto-encoder design based on a recurrent neural network
  • 3.3.3 Performance
  • 3.3.4 Distance-agnostic transceiver
  • 3.4 Implementation on a transmission link
  • 3.4.1 Conventional PAM transmission with ANN-based receiver
  • 3.4.2 Auto-encoder implementation
  • 3.5 Outlook
  • References
  • 4 Machine learning techniques for passive optical networks
  • 4.1 Background
  • 4.2 The validation of NN effectiveness
  • 4.3 NN for nonlinear equalization
  • 4.4 End to end deep learning for optimal equalization
  • 4.5 FPGA implementation of NN equalizer
  • 4.6 Conclusions and perspectives
  • References
  • 5 End-to-end learning for fiber-optic communication systems
  • 5.1 Introduction
  • 5.2 End-to-end learning
  • 5.3 End-to-end learning for fiber-optic communication systems
  • 5.3.1 Direct detection
  • 5.3.2 Coherent systems
  • 5.3.2.1 Nonlinear phase noise channel
  • 5.3.2.2 Perturbation models (NLIN and GN)
  • 5.3.2.3 Split-step Fourier method (SSFM)
  • 5.4 Gradient-free end-to-end learning
  • 5.5 Conclusion
  • Acknowledgments
  • References
  • 6 Deep learning techniques for optical monitoring
  • 6.1 Introduction
  • 6.2 Building blocks of deep learning-based optical monitors
  • 6.2.1 Digital coherent reception as a data-acquisition method
  • 6.2.2 Deep learning and representation learning
  • 6.2.3 Combination of digital coherent reception and deep learning
  • 6.3 Deep learning-based optical monitors
  • 6.3.1 Training mode of DL-based optical monitors
  • 6.3.2 Advanced topics for the training mode of DL-based optical monitors.
  • 6.3.2.1 Data augmentation based on domain knowledge of optical communication
  • 6.3.2.1.1 Data augmentation on polarization state
  • 6.3.2.1.2 Data augmentation on the frequency offset
  • 6.3.2.2 Transfer learning for adaptation of DNNs
  • 6.3.2.3 Federated learning for collaborative DNN training over multiple operators
  • 6.3.3 Inference mode of DL-based optical monitors
  • 6.3.4 Advanced topics for inference modes of DL-based optical monitors
  • 6.3.4.1 Cloud-based vs. edge-based implementations
  • 6.3.4.1.1 Cloud-based implementation of inference mode
  • 6.3.4.1.2 Edge-based implementation of inference mode
  • 6.3.4.2 Estimating the model uncertainty in inference mode
  • 6.4 Tips for designing DNNs for DL-based optical monitoring
  • 6.4.1 Shallow vs. deep network
  • 6.4.2 DNN architecture for optical monitoring
  • 6.4.2.1 Fully-connected DNNs
  • 6.4.2.2 Convolutional neural networks
  • 6.4.2.3 DNN architecture for the optical monitoring
  • 6.5 Experimental verifications
  • 6.5.1 Experimental setup for data collection
  • 6.5.2 Neural network architecture for OSNR estimation task
  • 6.5.2.1 DNN used in this experiment
  • DNN #1 (FC-DNN):
  • DNN #2 (CNN-1):
  • 6.5.2.2 Results and discussion
  • 6.5.3 Detailed experimental evaluation of CNN-based OSNR estimators
  • 6.5.3.1 DNN used in this section
  • DNN #3 (CNN-2):
  • 6.5.3.2 Results and discussion
  • 6.5.4 Versatile monitoring using DNN
  • 6.5.4.1 DNN architecture used in this experiment
  • DNN #4 (CNN-3):
  • 6.5.4.2 Results and discussion
  • 6.5.5 Data augmentation based on domain knowledge of optical transceivers
  • 6.5.5.1 DNN used in this section
  • DNN #5 (CNN-4):
  • 6.5.5.2 Results and discussion
  • 6.5.6 Estimating uncertainty by dropout at inference
  • 6.5.6.1 DNN used in this experiment
  • DNN #6 (CNN-5):
  • 6.5.6.2 Results and discussion.
  • 6.6 Future direction of data-analytic-based optical monitoring
  • 6.7 Summary
  • Acknowledgment
  • References
  • 7 Machine Learning methods for Quality-of-Transmission estimation
  • 7.1 Introduction
  • 7.2 Classification and regression models for QoT estimation
  • 7.2.1 Classification approaches for QoT estimation
  • 7.2.1.1 Performance evaluation metrics
  • ML classification
  • 7.2.1.2 Illustrative description of a classifier for QoT estimation
  • 7.2.2 Regression approaches for QoT estimation
  • 7.2.2.1 Regression models for QoT estimation
  • 7.3 Active and transfer learning approaches for QoT estimation
  • 7.3.1 Active learning
  • 7.3.1.1 Gaussian Processes for QoT estimation
  • 7.3.2 Transfer learning
  • 7.3.2.1 Domain adaptation techniques
  • 7.3.3 When to apply AL/DA during network lifecycle
  • 7.4 On the integration of ML in optimization tools
  • 7.4.1 RMSA integrating ML-based QoT estimation in EONs
  • 7.4.1.1 Integrated network planning framework
  • 7.5 Illustrative numerical results
  • 7.5.1 Data generation
  • 7.5.2 Classification
  • 7.5.3 Regression
  • 7.5.4 Active learning and transfer learning
  • 7.6 Future research directions and challenges
  • 7.7 Conclusion
  • References
  • 8 Machine Learning for optical spectrum analysis
  • 8.1 Introduction
  • 8.1.1 Failure detection and localization
  • 8.1.2 Optical spectrum
  • 8.1.3 Failures affecting the optical spectrum
  • 8.2 Feature-based spectrum monitoring
  • 8.2.1 Motivation and objectives
  • 8.2.2 OSA for soft-failure detection and identification
  • 8.2.2.1 Soft-failure detection, identification, and localization
  • 8.2.2.2 Options for classification using FeX
  • Multi-classifier approach
  • Single-classifier approach
  • Feature transformation for single-classifier approach
  • 8.2.3 Soft-failure localization
  • 8.2.4 Illustrative results
  • 8.2.4.1 VPI set-up for data collection.
  • 8.2.4.2 ML-based classification comparison
  • 8.2.4.3 Benefits of using a single OSA
  • 8.2.4.4 Benefits of feature transformation for classification
  • 8.2.4.5 Failure localization
  • 8.2.5 Conclusions
  • 8.3 Residual-based spectrum monitoring
  • 8.3.1 Residual-based approach for optical spectrum analysis
  • 8.3.2 Facilitating ML algorithm deployment using residual signals
  • 8.3.3 Illustrative results
  • 8.3.3.1 Comparison of residual-based and feature-based approaches
  • 8.3.3.2 The efficiency of residual adaptation mechanism
  • 8.3.4 Conclusions
  • 8.4 Monitoring of filterless optical networks
  • 8.4.1 Motivation of optical monitoring in FONs
  • 8.4.2 Signal identification and classification
  • 8.4.3 Optical signal tracking
  • 8.4.3.1 Feature-based tracking
  • Individual feature
  • Super features
  • 8.4.3.2 Residual-based tracking
  • 8.4.4 Illustrative results
  • 8.4.4.1 PAM4 scenario
  • 8.4.4.2 QPSK scenario
  • 8.4.5 Conclusions
  • 8.5 Concluding remarks and future work
  • List of acronyms
  • References
  • 9 Machine learning and data science for low-margin optical networks
  • 9.1 The shape of networks to come
  • 9.2 Current QoT margin taxonomy and design
  • 9.3 Generalization of optical network margins
  • 9.3.1 Optimal spectral efficiency
  • 9.3.2 Field margins
  • 9.3.3 Uncertainty margins
  • 9.3.4 Unallocated and implementation margins
  • 9.3.5 Protection margins
  • 9.3.6 Total spectral efficiency margin and QoT margin equivalency
  • 9.4 Large scale assessment of margins and their time variations in a deployed network
  • 9.4.1 Assessing the quality of transmission
  • 9.4.2 Description of the dataset
  • 9.4.3 Example of SNR variations in time
  • 9.4.4 Distributions of the minimal, maximal and median margins for all connections in the dataset
  • 9.4.5 System margins and long term performance variations.

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