Probabilistic low-margin optical-network design with multiple physical-layer parameter uncertainties
Oleg Karandin, Alessio Ferrari, Francesco Musumeci, Yvan Pointurier, Massimo Tornatore
Abstract
Analytical models for quality of transmission (QoT) estimation require safety design margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases these margins in the presence of multiple physical-layer uncertainties (namely, connector loss, erbium-doped fiber amplifier gain ripple, and fiber type) by leveraging monitoring data to build a probabilistic machine-learning-based QoT regressor. We evaluate the savings from margin reduction in terms of occupied spectrum and number of installed transponders in the C and C+L bands and demonstrate that 4%–12% transponder/spectrum savings can be achieved in realistic network instances by simply leveraging the SNR monitored at receivers and paying off a low increment in the lightpath disruption probability (at most 1%–4%).