Artificial Intelligence in Optical Networks and Systems
Description
This authoritative book, written by luminaries in the field, provides a comprehensive overview of the most recent research in the field of AI/ML applied to optical networks and systems use cases. The book then presents the most important tools and open datasets available to address the various use cases. The authors include relevant optical network architectures and technologies suitable for the development of AI applications, with focus on both inter- and intra-Data-Center scenarios. Emerging aspects of AI/ML applied to optical networks and systems, such as the role of Large Language Models, are also discussed. The book aims to provide a comprehensive overview of the application of AI/ML to typical optical networks and systems problems, as well as discuss new optical technologies and architectures that support the use of AI/ML in networked systems. Each chapter is authored by the most senior and active researchers in their respective subfields.
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Persons
Francesco Musumeci received the Ph.D. degree in Information Engineering from Politecnico di Milano, Italy, in 2013, where he is currently Associate Professor with the Department of Electronics, Information and Bioengineering. His current research interests include network data analysis and AI-assisted networking, design and optimization of communication networks, network disaster resilience, and converged space-ground network infrastructures. Dr. Musumeci is author of more than 170 papers published in international journals and conference proceedings, 3 book chapters and 1 patent in the area of communication networks, and is co-winner of three best paper awards from IEEE sponsored conferences. He is Associate Editor of IEEE TNSM and Springer PNET journals, and serves as a TPC co-chair, member and/or reviewer for several IEEE/OSA conferences as well as IEEE/OSA, Springer and Elsevier journals since 2010.
Qunbi Zhuge received his B.S. degree from Zhejiang University, China, in 2009, and his M.E. and Ph.D. degrees from McGill University, Canada, in 2012 and 2015, respectively. From 2014 to 2018, he was with Ciena Corporation, Canada. He is currently a Professor and Vice Dean of the School of Information Science and Electronic Engineering, Shanghai Jiao Tong University. He is an Optica Fellow. His research interests focus on optical interconnects, transmissions and networks for AI infrastructure. He has published over 310 academic papers. He is PI of dozens of national-level research grants and industrial collaboration projects. He has served as the Subcommittee Chair of OFC/CLEO/ACP/OECC, and editorial board member of journals including Optics Express and Science China Information Sciences. He has delivered tutorials and invited talks at OFC and ECOC. He has advised numerous students who have received prestigious international awards, such as the Grand Prize of the OFC Corning Outstanding Student Paper Competition, the IEEE Photonics Society Graduate Student Scholarship, and the Optica Corning Women in Optical Fiber Communications Scholarship.
Darli A. A. Mello earned his Ph.D. from UNICAMP in 2006. Following his doctoral studies, he joined Padtec S/A as a Senior Technology Engineer. From August 2008 to March 2014, he served as an Assistant Professor at the University of Brasilia (UnB). Since March 2014, he has held the position of Associate Professor in the Communications Department (DECOM) at the School of Electrical and Computer Engineering (FEEC) at UNICAMP. In addition, he was a Visiting Scholar at Stanford University from January 2019 to January 2020. Prof. Mello's primary research interests include optical transmission and networking. He has served as a Technical Program Committee (TPC) member for leading optical communications conferences such as OFC and ECOC. He has also chaired and co-chaired the TPC of several conferences, including IPC, SPPCOM, and LAOP. Dr. Mello is the co-author, alongside Fabio Barbosa, of the book Digital Coherent Optical Systems: Architecture and Algorithms, published by Springer Nature in 2021.
Content
Introduction.- PART I: AI for Optical Transmission Systems.- Optical Networks and Systems Datasets and Digital Twins for AI applications.- AI for QoT estimation and low-margin optical network design.- AI for long-haul transmission systems.- AI for short-reach and PON systems.- PART II: AI for Optical Networks.- AI for optical network control.- AI for optical network failure management.- AI for security management in optical networks.- AI for optical access networks.- The role of Large Language Models for Optical Network Automation.- PART III: Optical Networks for AI.- Inter- and Intra-DC AI: Optical Network architectures and protocols for distributed AI.- Conclusion.