To support the development of intelligent optical networks, accurate modeling of the physical
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …

High-accuracy physical layer models enable intelligent, self-driving optical networks. The
dynamic wavelength-dependent gain characteristics of erbium-doped fiber amplifiers …

Optical networks satisfy high bandwidth and low latency requirements for telecommunication
networks and data center interconnection. To improve network resource utilization, machine …

We propose a technique for modeling erbium-doped fiber amplifiers (EDFAs) in optical fiber
networks, where the amplifier unit is located at a distant node outside the laboratory. We …

Optical transmission systems provide high capacity, low latency and jitter, and high reliability
for city-scale networks. Recirculating loop experiments have facilitated the study of signal …

Relying on a two-measurement characterization, in this work a simple and effective gain
profile model for dual-stage optical erbium-doped fiber amplifiers (EDFAs) working under full …

Digital twins hold the promise of enabling efficient design and operation of complex optical
networks. They may be used as tools for offline system simulation and aid the design of …

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 …

To enable intelligent and self-driving optical networks, high-accuracy physical layer models
are required. The dynamic wavelength-dependent gain effects of non-constant-pump erbium …

Relying on a two-measurement characterization phase, a gain profile model for dual-stage
EDFAs is presented and validated in full spectral load condition. It precisely reproduces the …