This paper explores the emerging symbiosis between LLMs and optical networks. Massive LLMs require geo-distributed training, which demands advanced optical transport capabilities that require new key technical enablers, as WAN-aware CCL algorithms, ZR+ pluggables, and Hollow Core Fibers. Conversely, LLMs also enable new forms of autonomous network management.
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…
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…
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…
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…
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-…
We investigate selectively deploying bidirectional transmission in hybrid Hollow-Core Fiber (HCF) networks. Upgrading 50% of links to bidirectional HCF yields at least a 40% throughput increase compared to unidirectional SMF and captures 85% of the power consumption reduction of…