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Traffic demands in future elastic optical networks are expected to be heterogeneous with time-varying bandwidth. Estimating the physical-layer impairments (PLIs) for random bandwidth demands is important for cross-layer network resource provisioning. State-of-the-art PLI estimation techniques yield conservative PLI estimates using the maximum bandwidth, which leads to significant over-provisioning. This paper uses probabilistic information on random bandwidth demands to provide a computationally efficient, accurate, and flexible PLI estimate. The proposed model is consistent with the needs of future self-configuring fiber-optic networks and maximally avoids up to a 25% overestimation of PLIs compared to the benchmark for the cases studied, thus reducing the network design margin at a negligible extra computational cost. 

In long-haul fiber-optic networks, precise modeling of physical-layer impairments (PLIs) is crucial to optimizing network resource usage while ensuring adequate transmission quality. In order to accurately estimate PLIs, many mathematical models have been proposed. Among them, the so-called Gaussian noise (GN) model is one of the most accurate and simple enough to use on complex continental-size networks. However, the closed-form GN model assumes that the signals can be represented as having rectangular spectra, leading to a significant estimation error in typical cases when this assumption is violated. We propose the component-wise Gaussian noise (CWGN) PLI model that can account for arbitrary spectral-shaped demands. The CWGN model is computationally simple and suitable for most network management approaches. Results indicate that the CWGN model can prevent as much as a 136% overestimation of the PLIs resulting from the closed-form GN model applied to network lightpaths containing cascaded filters. 

A closed-form, highly accurate model estimates the cross-channel interference for arbitrary spectrum signals in long-haul fiber-optic transmission. It eliminates estimation errors of up to 37% resulting from assuming a rectangular spectrum for RRC signals. 

Flexible grid networks need rigorous resource planning to avoid network over-dimensioning and resource over-provisioning. The network must provision the hardware and spectrum resources statically, even for dynamic random bandwidth demands, due to the infrastructure of flexible grid networks, hardware limitations, and reconfiguration speed of the control plane. We propose a flexible online–offline probabilistic (FOOP) algorithm for the static spectrum assignment of random bandwidth demands. The FOOP algorithm considers the probabilistic nature of random bandwidth demands and balances hardware and control plane pressures with spectrum assignment efficiency. The FOOP algorithm uses the probabilistic spectrum Gaussian noise (PSGN) model to estimate the physical-layer impairment (PLI) for random bandwidth traffic. Compared to a benchmark spectrum assignment algorithm and a widely applied PLI estimation model, the proposed FOOP algorithm using the PSGN model saves up to 49% of network resources.