23 citations · 30 across the 8 of their papers we have counts for
8 papers
Agentic AI for Scalable and Robust Optical Systems Control
Zehao Wang, Mingzhe Han, Wei Cheng +12
We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural langu…
Optical Link Tomography: First Field Trial and 4D Extension
Takeo Sasai, Giacomo Borraccini, Yue-Kai Huang +11
Optical link tomography (OLT) is a rapidly evolving field that allows the multi-span, end-to-end visualization of optical power along fiber links in multiple dimensions from networ…
Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
Agastya Raj, Zehao Wang, Tingjun Chen +2
Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward mult…
Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks
Agastya Raj, Zehao Wang, Frank Slyne +3
We introduce a ML-based architecture for network operators to detect impairments from specific OSaaS users while blind to the users' internal spectrum details. Experimental studies…
Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework
Agastya Raj, Zehao Wang, Frank Slyne +3
We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth…
Semi-Automatic Line-System Provisioning with Integrated Physical-Parameter-Aware Methodology: Field Verification and Operational Feasibility
Hideki Nishizawa, Giacomo Borraccini, Takeo Sasai +17
We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologie…
Fast WDM provisioning with minimal probing: the first field experiments for DC exchanges
Hideki Nishizawa, Toru Mano, Thomas Ferreira De Lima +16
We propose an approach to estimate the end-to-end GSNR accurately in a short time when a data center interconnect (DCI) network operator receives a service request from users, not…
Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain
Agastya Raj, Zehao Wang, Frank Slyne +3
We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer…