Probabilistic low-margin optical-network design with multiple physical-layer parameter uncertainties

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%).

Non-technological barriers: the last frontier towards AI-powered intelligent optical networks

“…A prime example of legacy issues is the ML-aided QoT estimation 15 – 17 operation in optical networks, which has traditionally been dominated by non-ML approaches such as Gaussian noise (GN) model 18 and its variants 19 . ML-based QoT estimation methods, despite offering significant advantage in scenarios involving certain uncertainties about link parameters values 20 , 21 , have not been successful yet in achieving broad adoption in current fiber-optic networks and it may take a while before these techniques are deemed suitable substitutes for their legacy counterparts.…”

Optical path management based on machine learning for optical networks

“…Many ML technologies to identify and capture such characteristics have been presented in the last decade [21][22][23][24][25][26][27][28][29][30][31][32][33]. Most of these techniques use neural networks (NNs) to approximate the system response.…”

Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling

“…More recently, also partially loaded systems have been addressed [25,26]. The uncertainty in component param-eters and the effect on the QoT estimate has been investigated in [27,28]. Also methods addressing the uncertainty in component parameters have been proposed [29], e.g.…”