Thomas Roethig, Sleman Mouammar, Ítalo Brasileiro, Soheil Hosseini, Admela Jukan
We analyze the maximum burst size achievable in all-optical satellite networks across different constellations. With a 100 Gbps uplink capacity, a WDM-based optical burst switching network supports burst sizes of up to 500 MB in high-altitude LEO constellations and 600 MB in low-altitude LEO constellations.
Aruna Jayarajan, N. Cameron Matson, Karthikeyan Sundaresan
Low Earth Orbit (LEO) satellite constellations are becoming essential for expanding global Internet access, especially in remote and under-served areas. However, their highly dynamic nature, arising from network mobility, introduces complex coordination challenges between the dyn…
Shaoyou Ao, Yong Niu, Zhu Han, Cheng Li, Bo Ai
With the evolution of next-generation mobile communication networks and the commercial boom of Low Earth Orbit (LEO) satellites, globally covered satellite networks are gradually becoming a crucial infrastructure for massive user access and seamless connectivity. Accurate traffic…
Anuj Agrawal, Abhay Singh Dulta, Bhaskar Kanseri
Quantum network implementations using single spontaneous parametric downconversion (SPDC)-based broadband entangled photon pair source (EPPS) have been reported recently. Here, leveraging the wavelength-correlation between entangled photon pairs, the traditional wavelength divisi…
Kiarash Rezaei, Omran Ayoub, Paolo Monti, Carlos Natalino
Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuGLEN, a stepwise evaluation pipeline that uses an LLM-as-a-judge together with a small set of expert…
Omran Ayoub, Carlos Natalino, Ali Al Housseini, Felix Foschum, Philipp Morger, Tiziano Leidi, et al.
Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, performance monitoring and resource allocation. In these environments, ML-driven predictions may be directly coupled with control-plane…
Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub
The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks. Models trained on data from a specific topology or operational configuration often exhibit degraded performance when deployed in unseen networks. In t…