arXiv subjects
Publications and source records attributed to Mohammad M. Hosseini.
We analyze polarization data streamed from a commercial coherent transponder, used in MedNautilus link, during 2023 Turkey earthquakes. We show how earthquake features and cable geometry fundamentally govern the optical polarization response, taking the first steps for detailed interpretation in designing fiber-based seismic sensing systems.
State-of-Polarization sensing with coherent transponders enables wide-area geophysical monitoring over existing submarine cables, but its performance is limited by polarization noise from both the fiber and terminal hardware. This work investigates how terminal noise affects polarization rotation estimates derived from receiver equalizer coefficients and how it can obscure stochastic polarization drift used for sensing. We analyze Jones-matrix time series from two deployed receivers in the Sparkle Mediterranean link MedNautilus (approximately 2000 km and 450 km) and compare them with a laboratory back-to-back reference. Power Spectral Density (PSD) analysis reveals a low-frequency random-walk regime and a high-frequency white-noise floor, separated by a link-dependent corner frequency. The rotation innovation variance increases with link length, while the longest field link also shows elevated white noise consistent with accumulated amplifier and terminal contributions. Additionally, harmonic spectral components are observed, indicating a transponder-related artifact that should be considered in practical sensing applications.
The convergence of metro and access networks into unified optical infrastructures requires cost-effective alternatives to conventional solid-core fiber (SCF). This paper examines using hollow-core fiber (HCF) with digital subcarrier multiplexing (DSCM) transceivers in horseshoe-and-spur filterless optical architectures. Leveraging HCF's ultra-low nonlinearity, we optimize amplifier placement to maximize power budgets under realistic constraints. Our results show that HCF shifts the main limitation from nonlinearity to amplifier output power, enabling up to a 20 dB spur power-budget gain over SCF. Considering balanced and unbalanced couplers, we find that increasing amplifier density boosts reach only up to a saturation point (about 11-13 units in a 5-transit-node network). A techno-economic break-even analysis of hybrid SCF-HCF deployments shows that targeted HCF use provides intermediate performance gains and can fully recover its fiber cost premium through amplifier reductions.
Machine learning-based failure management in optical networks has gained significant attention in recent years. However, severe class imbalance, where normal instances vastly outnumber failure cases, remains a considerable challenge. While pre- and in-processing techniques have been widely studied, post-processing methods are largely unexplored. In this work, we present a direct comparison of pre-, in-, and post-processing approaches for class imbalance mitigation in failure detection and identification using an experimental dataset. For failure detection, post-processing methods-particularly Threshold Adjustment-achieve the highest F1 score improvement (up to 15.3%), while Random Under-Sampling provides the fastest inference. In failure identification, GenAI methods deliver the most substantial performance gains (up to 24.2%), whereas post-processing shows limited impact in multi-class settings. When class overlap is present and latency is critical, over-sampling methods such as the SMOTE are most effective; without latency constraints, Meta-Learning yields the best results. In low-overlap scenarios, Generative AI approaches provide the highest performance with minimal inference time.
We compare pre-, in-, and post-processing techniques for class imbalance mitigation in optical network failure detection. Threshold Adjustment achieves the highest F1 gain (15.3%), while Random Under-sampling (RUS) offers the fastest inference, highlighting a key performance-complexity trade-off.