Machine learning for future fiber-optic communication systems:
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Kidlington, Oxford, United Kingdom
Academic Press
[2022]
|
| Online-Zugang: | TUM01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (xvi, 383 Seiten) Illustrationen, Diagramme |
| ISBN: | 9780323852289 |
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| 245 | 1 | 0 | |a Machine learning for future fiber-optic communication systems |c edited by Alan Pak Tao Lau, Faisal Nadeem Khan |
| 264 | 1 | |a London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Kidlington, Oxford, United Kingdom |b Academic Press |c [2022] | |
| 264 | 4 | |c © 2022 | |
| 300 | |a 1 Online-Ressource (xvi, 383 Seiten) |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 9.4.6 Distribution of long term SNR variations | |
| 700 | 1 | |a Lau, Alan Pak Tao |4 edt | |
| 700 | 1 | |a Khan, Faisal Nadeem |0 (DE-588)1146857152 |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |a Lau, Alan Pak Tao |t Machine Learning for Future Fiber-Optic Communication Systems |d San Diego : Elsevier Science & Technology,c2022 |n Druck-Ausgabe |z 978-0-323-85227-2 |
| 912 | |a ZDB-30-PQE | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033602588 | ||
| 966 | e | |u https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6886869 |l TUM01 |p ZDB-30-PQE |q TUM_PDA_PQE_Kauf |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804184003043917824 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author2 | Lau, Alan Pak Tao Khan, Faisal Nadeem |
| author2_role | edt edt |
| author2_variant | a p t l apt aptl f n k fn fnk |
| author_GND | (DE-588)1146857152 |
| author_facet | Lau, Alan Pak Tao Khan, Faisal Nadeem |
| building | Verbundindex |
| bvnumber | BV048221851 |
| classification_tum | ELT 683 |
| collection | ZDB-30-PQE |
| 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 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 9.4.6 Distribution of long term SNR variations |
| ctrlnum | (ZDB-30-PQE)EBC6886869 (ZDB-30-PAD)EBC6886869 (ZDB-89-EBL)EBL6886869 (OCoLC)1299386363 (DE-599)BVBBV048221851 |
| dewey-full | 621.382750285631 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.382750285631 |
| dewey-search | 621.382750285631 |
| dewey-sort | 3621.382750285631 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Electronic eBook |
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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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">9.4.6 Distribution of long term SNR variations</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lau, Alan Pak Tao</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khan, Faisal Nadeem</subfield><subfield code="0">(DE-588)1146857152</subfield><subfield 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| id | DE-604.BV048221851 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:32Z |
| indexdate | 2024-07-10T09:32:25Z |
| institution | BVB |
| isbn | 9780323852289 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033602588 |
| oclc_num | 1299386363 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xvi, 383 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Academic Press |
| record_format | marc |
| spelling | Machine learning for future fiber-optic communication systems edited by Alan Pak Tao Lau, Faisal Nadeem Khan London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Kidlington, Oxford, United Kingdom Academic Press [2022] © 2022 1 Online-Ressource (xvi, 383 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 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 9.4.6 Distribution of long term SNR variations Lau, Alan Pak Tao edt Khan, Faisal Nadeem (DE-588)1146857152 edt Erscheint auch als Lau, Alan Pak Tao Machine Learning for Future Fiber-Optic Communication Systems San Diego : Elsevier Science & Technology,c2022 Druck-Ausgabe 978-0-323-85227-2 |
| spellingShingle | Machine learning for future fiber-optic communication systems 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 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 9.4.6 Distribution of long term SNR variations |
| title | Machine learning for future fiber-optic communication systems |
| title_auth | Machine learning for future fiber-optic communication systems |
| title_exact_search | Machine learning for future fiber-optic communication systems |
| title_exact_search_txtP | Machine learning for future fiber-optic communication systems |
| title_full | Machine learning for future fiber-optic communication systems edited by Alan Pak Tao Lau, Faisal Nadeem Khan |
| title_fullStr | Machine learning for future fiber-optic communication systems edited by Alan Pak Tao Lau, Faisal Nadeem Khan |
| title_full_unstemmed | Machine learning for future fiber-optic communication systems edited by Alan Pak Tao Lau, Faisal Nadeem Khan |
| title_short | Machine learning for future fiber-optic communication systems |
| title_sort | machine learning for future fiber optic communication systems |
| work_keys_str_mv | AT laualanpaktao machinelearningforfuturefiberopticcommunicationsystems AT khanfaisalnadeem machinelearningforfuturefiberopticcommunicationsystems |