01 Jan 0001
00:00 - 00:00
This panel addresses the critical need for an effective selection framework for machine learning (ML) algorithms in optical communication networks. ML shows great potential in challenging tasks such as nonlinearity compensation, network performance monitoring, and fault management. The plethora of state-of-the-art ML algorithms makes choosing the best fit difficult. What data is used for training and evaluation is equally important. Bringing together researchers, industry experts, and network operators, the panel will review current ML applications, highlighting benefits and challenges.
The key points in this panel will be:
- The discussion will focus on suitability and limitations of different ML algorithms for key optical communication tasks, aiming to establish selection criteria for dataset quality, performance, interpretability, efficiency, and scalability.
- The scope will include ML-based signal processing, transmission data analysis, network optimization, and trends such as photonic neural networks and standardized ML models.
- The goal is to provide practical guidelines and foster collaboration, ultimately advancing intelligent, ML-driven optical networks towards real-world demands.
Organizers
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Boris Karanov
Karlsruher Institut für Technologie, Germany
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Lakshmi Narasimhan
Indian Institute of Technology, India
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Deepa Venkitesh
Indian Institute of Technology Madras, India