Accurate prediction of quality of transmission based on a dynamically configurable optical impairment model

We have proposed a dynamically configurable and fast optical impairment model for the abstraction of the optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict optical signal quality at the receiver of not-already-established optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by impairment-aware network simulation.