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1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Contents |
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 & -- 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. |
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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. |
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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. |
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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. |
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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. |
| Subject |
Optical fiber communication.
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Machine learning.
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Télécommunications par fibres optiques.
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Apprentissage automatique.
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Machine learning
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Optical fiber communication
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| Added Author |
Lau, Alan Pak Tao.
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Kham, Faisal Nadeem.
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| Other Form: |
Print version: 0323852270 9780323852272 (OCoLC)1281239375 |
| ISBN |
9780323852289 electronic book |
|
0323852289 electronic book |
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9780323852272 |
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0323852270 |
| Standard No. |
AU@ 000071341249 |
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AU@ 000074353259 |
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