Optical Network Technologies

Últimos artículos

• LDPKiT: Superimposing Remote Queries for Privacy-Preserving Distillation

  Kexin Li, Aastha Mehta, David Lie · 13 de julio de 2026

  To protect privacy in regulated domains such as healthcare and finance, model owners may allow only remote API access while keeping both the training data and model parameters private. However, model users performing inference on such remotely hosted models may be required to transmit potentially se…

• HEM: a margin-based loss for visual categorisation tasks

  Michael W. Spratling, Heiko H. Sch\"utt · 23 de junio de 2026

  Training deep neural networks (DNNs) on classification tasks can be performed with a number of different losses, but cross-entropy (CE) loss is the de-facto standard. Here, we propose an alternative loss, high error margin (HEM), which is a margin based loss modified to improve the training dynamics…

• Optimization of CV-QKD Under Practical Constraints

  Svitlana Matsenko, Amirhossein Ghazisaeidi, Marcin Jarzyna, Konrad Banaszek, Darko Zibar · 6 de mayo de 2026

  Using reinforcement learning, we optimize for practical hardware constraints, including limited FIR filter taps at the transmitter and receiver, mean photon number and finite DAC/ADC resolution. Under these realistic conditions, the proposed approach achieves significant performance improvements.…

• Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring

  Leyla Sadighi, Stefan Karlsson, Carlos Natalino, Mojtaba Eshghie, Fehmida Usmani, Eoin Kenny, Lena Wosinska, Paolo Monti, Marija Furdek, Marco Ruffini · 21 de abril de 2026

  Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a different system, due to variations in operating wavelength, fiber properties, and network architecture. To overcome this, we propose a Domain Adaptation (D…

• Transformer-Based Prognostics: Enhancing Network Availability by Improved Monitoring of Optical Fiber Amplifiers

  Dominic Schneider, Lutz Rapp, Christoph Ament · 31 de marzo de 2026

  We enhance optical network availability and reliability through a lightweight transformer model that predicts optical fiber amplifier lifetime from condition-based monitoring data, enabling real-time, edge-level predictive maintenance and advancing deployable AI for autonomous network operation.…

• Half the Nonlinearity Is Wasted: Measuring and Reallocating the Transformer's MLP Budget

  Peter Balogh · 10 de marzo de 2026

  We investigate when transformer MLP nonlinearity is actually necessary. A gate with $d+1$ parameters decides when to replace the full MLP with a linear surrogate. Through systematic investigation across six models (162M-2.8B parameters), two architectures, and three corpora, we establish that nonlin…

• Half the Nonlinearity Is Wasted: Measuring and Reallocating the Transformer's MLP Budget

  Peter Balogh · 5 de marzo de 2026

  We investigate when transformer MLP nonlinearity is actually necessary. A gate with $d+1$ parameters decides when to replace the full MLP with a linear surrogate. Through systematic investigation across six models (162M-2.8B parameters), two architectures, and three corpora, we establish that nonlin…

• Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels

  Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi · 4 de febrero de 2026

  We introduce neural probabilistic amplitude shaping, a joint-distribution learning framework for coherent fiber systems. The proposed scheme provides a 0.5 dB signal-to-noise ratio gain over sequence selection for dual-polarized 64-QAM transmission across a single-span 205 km link.…

• Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission

  Sergio Hernandez, Christophe Peucheret, Francesco Da Ros, Darko Zibar · 29 de diciembre de 2025

  Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dynamics make the modeling and optimization of DML-based systems challenging. In this paper, we study the end-to-end optimiz…