As optical networks scale beyond the C+L bands, the role of optical amplifiers (OAs) becomes increasingly critical. While Erbium-Doped Fiber Amplifiers (EDFAs) remain the go-to solution for C and L bands, extending amplification further requires new rare-earth...
Exploring the Impact of MB Loss Profile and SRS on GSNR
In our latest study, we dive deep into the effects of Stimulated Raman Scattering (SRS) on multi-band optical transport. Looking we analyze the GSNR and its OSNR and SNR_NL components for a 70 km scenario, revealing key insights: 🔹 Without SRS (dot-dashed curves), the...
Optical Transport Digital Model in PHY-DT
In optical networking, the transport system consists of transparent WDM light paths (LPs) that need to be optimized across the network topology. A key challenge in modeling these paths is accounting for signal impairments caused by nonlinear fiber effects, amplified...
Physical Layer Digital Twin in Optical Transport
In optical networking, the physical layer (PHY) is the foundation—comprising network elements (NEs) and devices that enable transparent optical circuits. To optimize performance, we need a Digital Twin (DT) that accurately models the impact of each NE on the...
Digital Twin & Modeling in Optical Networks
The Digital Twin (DT) concept, first introduced by NASA, has evolved into a powerful tool across various industries. In essence, a DT is a virtual replica of a physical system, using real-time data and simulations to optimize performance and decision-making. 🔹 Why...
Complexity Reduction of Neural Networks Using Multi-Task Learning (MTL)
As long-haul coherent-detection DWDM optical transmission systems push the limits of spectral and power efficiency, developing energy-efficient NN-based nonlinear equalizers (NLEs) is critical. One promising approach? Multi-Task Learning (MTL). 🔹 What is MTL?MTL...
Evaluating the Benefits of Computational Complexity Reduction in Neural Networks
In our latest study, we explore the impact of computational complexity (CC) reduction in NN-based nonlinear equalizers (NLEs) for optical communication. Using a numerically simulated single-carrier 64-QAM 30 GBd dual-polarization channel over 20×50 km of SSMF, we...
Complexity Reduction in Neural Network Equalizers
Optimizing computational complexity (CC) is key to deploying efficient NN equalizers on resource-constrained hardware. To achieve this, we focus on three key areas: (i) training, (ii) inference, and (iii) hardware synthesis. 🔹 Training EfficiencyReducing CC in...
Optimizing Optical & Digital Signal Processing for Multi-Core Fibres & Optical MIMO in SDM-PON
As demand for high-speed data transmission grows, energy efficiency in optical communication systems is more critical than ever. In the ALLEGRO project, we are exploring advanced DSP techniques to optimize performance while minimizing power consumption. 🔍 Key...
Advancing the Packaging of ALLEGRO Devices & Subsystems
In the ALLEGRO project, packaging plays a crucial role in ensuring optimal performance and integration of our advanced photonic and electronic devices. As detailed in the RP1 Technical Report, Fraunhofer IZM has collaborated closely with IPR, TUe, NVIDIA, and ETHZ to...