Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

An ultra-fast machine learning based method for accurate predictions of gain and amplified spontaneous emission (ASE) noise profiles of Raman amplifiers is introduced. It is an alternative to high-complexity and time-consuming standard approaches, which are based on the numerical solution of sets of nonlinear differential equations. Main relevance resides on its possible application in real-time network controllers for future multi-band optical line systems where Raman amplification will be required to copewith capacities beyond the standard C-band. Here we consider as an example the C+L-band scenario with different input load conditions: full load and partial loads. For the case of full load it has been recently shown a neural network (NN) capable of highly accurate predictions. Real optical networks are not usually operated only in full load conditions: the load can dynamically vary over time and the behavior of the Raman amplifier depends on it. In this article we introduce a new NNmodel and we show its higher accuracy when the line system is not fully loaded: we define it as the load aware neural network. Applying this new approachwe can predict both gain and ASE noise profiles in Raman amplifiers with high accuracy under any load conditions: we demonstrate almost 100% of maximum prediction errors to be lower than 0.5 dB.