FIBER-OPTIC COMMUNICATION SYSTEMS
CONTENTS 1 INTRODUCTION TO MACHINE LEARNING TECHNIQUES: AN OPTICAL COMMUNICATION’S PERSPECTIVE 1 1 INTRODUCTION 1 2 SUPERVISED LEARNING 1 3 UNSUPERVISED LEARNING 1 4 REINFORCEMENT LEARNING (RL) 1 5 DEEP LEARNING TECHNIQUES 1 6 FUTURE ROLE OF ML IN OPTICAL COMMUNICATIONS 1 7 ONLINE RESOURCES FOR ML ALGORITHMS 1 8 CONCLUSIONS APPENDIX 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 3 APPLICATION OF MACHINE LEARNING IN DIGITAL BACKPROPAGATION 2 4 OUTLOOK OF MACHINE LEARNING IN LONG-HAUL SYSTEMS 3 MACHINE LEARNING FOR SHORT REACH OPTICAL ?BER 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 4 IMPLEMENTATION ON A TRANSMISSION LINK 3 5 OUTLOOK 4 MACHINE LEARNING TECHNIQUES FOR PASSIVE OPTICAL NETWORKS 4 1 BACKGROUND 4 2 THE VALIDATION OF NN E?ECTIVENESS 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 5 END-TO-END LEARNING FOR ?BER-OPTIC COMMUNICATION SYSTEMS 5 1 INTRODUCTION 5 2 END-TO-END LEARNING 5 3 END-TO-END LEARNING FOR ?BER-OPTIC COMMUNICATION SYSTEMS 5 4 GRADIENT-FREE END-TO-END LEARNING 5 5 CONCLUSION ACKNOWLEDGMENTS 6 DEEP LEARNING TECHNIQUES FOR OPTICAL MONITORING 6 1 INTRODUCTION 6 2 BUILDING BLOCKS OF DEEP LEARNING-BASED OPTICAL MONITORS 6 3 DEEP LEARNING-BASED OPTICAL MONITORS 6 4 TIPS FOR DESIGNING DNNS FOR DL-BASED OPTICAL MONITORING 6 5 EXPERIMENTAL VERI?CATIONS 6 6 FUTURE DIRECTION OF DATA-ANALYTIC-BASED OPTICAL MONITORING 6 7 SUMMARY ACKNOWLEDGMENT 7 MACHINE LEARNING METHODS FOR QUALITY-OF-TRANSMISSION ESTIMATION 7 1 INTRODUCTION 7 2 CLASSI?CATION AND REGRESSION MODELS FOR QOT ESTIMATION 7 3 ACTIVE AND TRANSFER LEARNING APPROACHES FOR QOT ESTIMATION 7 4 ON THE INTEGRATION OF ML IN OPTIMIZATION TOOLS 7 5 ILLUSTRATIVE NUMERICAL RESULTS 7 6 FUTURE RESEARCH DIRECTIONS AND CHALLENGES 7 7 CONCLUSION 8 MACHINE LEARNING FOR OPTICAL SPECTRUM ANALYSIS 8 1 INTRODUCTION 8 2 FEATURE-BASED SPECTRUM MONITORING 8 3 RESIDUAL-BASED SPECTRUM MONITORING 8 4 MONITORING OF ?LTERLESS OPTICAL NETWORKS 8 5 CONCLUDING REMARKS AND FUTURE WORK LIST OF ACRONYMS 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 4 LARGE SCALE ASSESSMENT OF MARGINS AND THEIR TIME VARIATIONS IN A DEPLOYED NETWORK 9 5 TRADE-O?BETWEEN CAPACITY AND AVAILABILITY 9 6 DATA-DRIVEN RATE ADAPTATION FOR AUTOMATED NETWORK UPGRADES 9 7 MACHINE LEARNING FOR LOW-MARGIN OPTICAL NETWORKS 9 8 CONCLUSION 10 MACHINE LEARNING FOR NETWORK SECURITY MANAGEMENT, ATTACKS, AND INTRUSIONS DETECTION 10 1 PHYSICAL LAYER SECURITY MANAGEMENT 10 2 MACHINE LEARNING TECHNIQUES FOR SECURITY DIAGNOSTICS 10 3 ACCURACY OF ML MODELS IN THREAT DETECTION 10 4 RUNTIME COMPLEXITY OF ML MODELS 10 5 INTERPRETABILITY OF ML MODELS 10 6 OPEN CHALLENGES 10 7 CONCLUSION 11 MACHINE LEARNING FOR DESIGN AND OPTIMIZATION OF PHOTONIC DEVICES 11 1 INTRODUCTION 11 2 DEEP NEURAL NETWORK (DNN) MODELS 11 3 NANOPHOTONIC POWER SPLITTER 11 4 METASURFACES AND PLASMONICS 11 5 OTHER TYPES OF OPTICAL DEVICES 11 6 DISCUSSION 11 7 CONCLUSION